{
 "cells": [
  {
   "cell_type": "code",
   "execution_count": 1,
   "id": "9a5fab2e-75e4-45f0-a158-6c0484664513",
   "metadata": {},
   "outputs": [],
   "source": [
    "# pytorch\n",
    "import torch\n",
    "import torch.nn as nn\n",
    "# Mobile Net\n",
    "from torchvision.models.quantization.mobilenetv3 import mobilenet_v3_large\n",
    "from torch.quantization import prepare_qat, get_default_qat_qconfig, convert\n",
    "from torchvision.models import quantization\n",
    "from torch.quantization import QuantStub, DeQuantStub, quantize_dynamic, prepare_qat, convert\n",
    "# dataset\n",
    "from torchvision import datasets\n",
    "from torchvision import transforms\n",
    "# dataloader\n",
    "from torch.utils.data import DataLoader\n",
    "# Util\n",
    "import time\n",
    "import datetime\n",
    "import numpy as np\n",
    "import matplotlib.pyplot as plt\n",
    "import copy\n",
    "# tensorboard\n",
    "from torch.utils.tensorboard import SummaryWriter\n",
    "# plt\n",
    "import matplotlib\n",
    "import matplotlib.pyplot as plt\n",
    "plt.rcParams['font.family'] = 'Noto Sans CJK JP'\n",
    "matplotlib.rcParams.update({'font.size': 18})"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 2,
   "id": "61e43e83",
   "metadata": {},
   "outputs": [],
   "source": [
    "# 超参数\n",
    "input_size = 224\n",
    "batch_size = 32\n",
    "n_worker = 8\n",
    "lr = 0.001\n",
    "epochs = 50"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 3,
   "id": "916c1c6f-6228-4d64-928d-68c045094bd4",
   "metadata": {},
   "outputs": [],
   "source": [
    "# 生成训练数据集\n",
    "train_path = \"image/train_image\"\n",
    "test_path = \"image/test_image\"\n",
    "data_transform = transforms.Compose([\n",
    "        transforms.Resize([input_size, input_size]),\n",
    "        transforms.ToTensor(),\n",
    "])\n",
    "train_dataset = datasets.ImageFolder(train_path, transform=data_transform)\n",
    "test_dataset = datasets.ImageFolder(test_path, transform=data_transform)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 4,
   "id": "63a6157c-189f-46fc-9698-f8b99af5efe6",
   "metadata": {},
   "outputs": [],
   "source": [
    "# 生成数据加载器\n",
    "train_loader = DataLoader(\n",
    "    train_dataset, batch_size=batch_size, shuffle=True,\n",
    "    num_workers=n_worker, pin_memory=True)\n",
    "test_loader = DataLoader(\n",
    "    test_dataset, batch_size=20, shuffle=False, \n",
    "    num_workers=n_worker)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 5,
   "id": "4c7b0afc",
   "metadata": {},
   "outputs": [],
   "source": [
    "# 定义模型和优化器\n",
    "model = mobilenet_v3_large()\n",
    "model.classifier[3] = nn.Linear(1280, 90)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 6,
   "id": "30800540",
   "metadata": {},
   "outputs": [],
   "source": [
    "model.fuse_model()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 7,
   "id": "88975106",
   "metadata": {},
   "outputs": [
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "/data/lambd4/anaconda3/py39/lib/python3.9/site-packages/torch/ao/quantization/observer.py:214: UserWarning: Please use quant_min and quant_max to specify the range for observers.                     reduce_range will be deprecated in a future release of PyTorch.\n",
      "  warnings.warn(\n"
     ]
    },
    {
     "data": {
      "text/plain": [
       "QuantizableMobileNetV3(\n",
       "  (features): Sequential(\n",
       "    (0): Conv2dNormActivation(\n",
       "      (0): ConvBn2d(\n",
       "        3, 16, kernel_size=(3, 3), stride=(2, 2), padding=(1, 1), bias=False\n",
       "        (bn): BatchNorm2d(16, eps=0.001, momentum=0.01, affine=True, track_running_stats=True)\n",
       "        (weight_fake_quant): FusedMovingAvgObsFakeQuantize(\n",
       "          fake_quant_enabled=tensor([1]), observer_enabled=tensor([1]), scale=tensor([1.]), zero_point=tensor([0], dtype=torch.int32), dtype=torch.qint8, quant_min=-128, quant_max=127, qscheme=torch.per_channel_symmetric, reduce_range=False\n",
       "          (activation_post_process): MovingAveragePerChannelMinMaxObserver(min_val=tensor([]), max_val=tensor([]))\n",
       "        )\n",
       "        (activation_post_process): FusedMovingAvgObsFakeQuantize(\n",
       "          fake_quant_enabled=tensor([1]), observer_enabled=tensor([1]), scale=tensor([1.]), zero_point=tensor([0], dtype=torch.int32), dtype=torch.quint8, quant_min=0, quant_max=127, qscheme=torch.per_tensor_affine, reduce_range=True\n",
       "          (activation_post_process): MovingAverageMinMaxObserver(min_val=inf, max_val=-inf)\n",
       "        )\n",
       "      )\n",
       "      (1): Identity()\n",
       "      (2): Hardswish(\n",
       "        (activation_post_process): FusedMovingAvgObsFakeQuantize(\n",
       "          fake_quant_enabled=tensor([1]), observer_enabled=tensor([1]), scale=tensor([1.]), zero_point=tensor([0], dtype=torch.int32), dtype=torch.quint8, quant_min=0, quant_max=127, qscheme=torch.per_tensor_affine, reduce_range=True\n",
       "          (activation_post_process): MovingAverageMinMaxObserver(min_val=inf, max_val=-inf)\n",
       "        )\n",
       "      )\n",
       "    )\n",
       "    (1): QuantizableInvertedResidual(\n",
       "      (block): Sequential(\n",
       "        (0): Conv2dNormActivation(\n",
       "          (0): ConvBnReLU2d(\n",
       "            16, 16, kernel_size=(3, 3), stride=(1, 1), padding=(1, 1), groups=16, bias=False\n",
       "            (bn): BatchNorm2d(16, eps=0.001, momentum=0.01, affine=True, track_running_stats=True)\n",
       "            (weight_fake_quant): FusedMovingAvgObsFakeQuantize(\n",
       "              fake_quant_enabled=tensor([1]), observer_enabled=tensor([1]), scale=tensor([1.]), zero_point=tensor([0], dtype=torch.int32), dtype=torch.qint8, quant_min=-128, quant_max=127, qscheme=torch.per_channel_symmetric, reduce_range=False\n",
       "              (activation_post_process): MovingAveragePerChannelMinMaxObserver(min_val=tensor([]), max_val=tensor([]))\n",
       "            )\n",
       "            (activation_post_process): FusedMovingAvgObsFakeQuantize(\n",
       "              fake_quant_enabled=tensor([1]), observer_enabled=tensor([1]), scale=tensor([1.]), zero_point=tensor([0], dtype=torch.int32), dtype=torch.quint8, quant_min=0, quant_max=127, qscheme=torch.per_tensor_affine, reduce_range=True\n",
       "              (activation_post_process): MovingAverageMinMaxObserver(min_val=inf, max_val=-inf)\n",
       "            )\n",
       "          )\n",
       "          (1): Identity()\n",
       "          (2): Identity()\n",
       "        )\n",
       "        (1): Conv2dNormActivation(\n",
       "          (0): ConvBn2d(\n",
       "            16, 16, kernel_size=(1, 1), stride=(1, 1), bias=False\n",
       "            (bn): BatchNorm2d(16, eps=0.001, momentum=0.01, affine=True, track_running_stats=True)\n",
       "            (weight_fake_quant): FusedMovingAvgObsFakeQuantize(\n",
       "              fake_quant_enabled=tensor([1]), observer_enabled=tensor([1]), scale=tensor([1.]), zero_point=tensor([0], dtype=torch.int32), dtype=torch.qint8, quant_min=-128, quant_max=127, qscheme=torch.per_channel_symmetric, reduce_range=False\n",
       "              (activation_post_process): MovingAveragePerChannelMinMaxObserver(min_val=tensor([]), max_val=tensor([]))\n",
       "            )\n",
       "            (activation_post_process): FusedMovingAvgObsFakeQuantize(\n",
       "              fake_quant_enabled=tensor([1]), observer_enabled=tensor([1]), scale=tensor([1.]), zero_point=tensor([0], dtype=torch.int32), dtype=torch.quint8, quant_min=0, quant_max=127, qscheme=torch.per_tensor_affine, reduce_range=True\n",
       "              (activation_post_process): MovingAverageMinMaxObserver(min_val=inf, max_val=-inf)\n",
       "            )\n",
       "          )\n",
       "          (1): Identity()\n",
       "        )\n",
       "      )\n",
       "      (skip_add): FloatFunctional(\n",
       "        (activation_post_process): FusedMovingAvgObsFakeQuantize(\n",
       "          fake_quant_enabled=tensor([1]), observer_enabled=tensor([1]), scale=tensor([1.]), zero_point=tensor([0], dtype=torch.int32), dtype=torch.quint8, quant_min=0, quant_max=127, qscheme=torch.per_tensor_affine, reduce_range=True\n",
       "          (activation_post_process): MovingAverageMinMaxObserver(min_val=inf, max_val=-inf)\n",
       "        )\n",
       "      )\n",
       "    )\n",
       "    (2): QuantizableInvertedResidual(\n",
       "      (block): Sequential(\n",
       "        (0): Conv2dNormActivation(\n",
       "          (0): ConvBnReLU2d(\n",
       "            16, 64, kernel_size=(1, 1), stride=(1, 1), bias=False\n",
       "            (bn): BatchNorm2d(64, eps=0.001, momentum=0.01, affine=True, track_running_stats=True)\n",
       "            (weight_fake_quant): FusedMovingAvgObsFakeQuantize(\n",
       "              fake_quant_enabled=tensor([1]), observer_enabled=tensor([1]), scale=tensor([1.]), zero_point=tensor([0], dtype=torch.int32), dtype=torch.qint8, quant_min=-128, quant_max=127, qscheme=torch.per_channel_symmetric, reduce_range=False\n",
       "              (activation_post_process): MovingAveragePerChannelMinMaxObserver(min_val=tensor([]), max_val=tensor([]))\n",
       "            )\n",
       "            (activation_post_process): FusedMovingAvgObsFakeQuantize(\n",
       "              fake_quant_enabled=tensor([1]), observer_enabled=tensor([1]), scale=tensor([1.]), zero_point=tensor([0], dtype=torch.int32), dtype=torch.quint8, quant_min=0, quant_max=127, qscheme=torch.per_tensor_affine, reduce_range=True\n",
       "              (activation_post_process): MovingAverageMinMaxObserver(min_val=inf, max_val=-inf)\n",
       "            )\n",
       "          )\n",
       "          (1): Identity()\n",
       "          (2): Identity()\n",
       "        )\n",
       "        (1): Conv2dNormActivation(\n",
       "          (0): ConvBnReLU2d(\n",
       "            64, 64, kernel_size=(3, 3), stride=(2, 2), padding=(1, 1), groups=64, bias=False\n",
       "            (bn): BatchNorm2d(64, eps=0.001, momentum=0.01, affine=True, track_running_stats=True)\n",
       "            (weight_fake_quant): FusedMovingAvgObsFakeQuantize(\n",
       "              fake_quant_enabled=tensor([1]), observer_enabled=tensor([1]), scale=tensor([1.]), zero_point=tensor([0], dtype=torch.int32), dtype=torch.qint8, quant_min=-128, quant_max=127, qscheme=torch.per_channel_symmetric, reduce_range=False\n",
       "              (activation_post_process): MovingAveragePerChannelMinMaxObserver(min_val=tensor([]), max_val=tensor([]))\n",
       "            )\n",
       "            (activation_post_process): FusedMovingAvgObsFakeQuantize(\n",
       "              fake_quant_enabled=tensor([1]), observer_enabled=tensor([1]), scale=tensor([1.]), zero_point=tensor([0], dtype=torch.int32), dtype=torch.quint8, quant_min=0, quant_max=127, qscheme=torch.per_tensor_affine, reduce_range=True\n",
       "              (activation_post_process): MovingAverageMinMaxObserver(min_val=inf, max_val=-inf)\n",
       "            )\n",
       "          )\n",
       "          (1): Identity()\n",
       "          (2): Identity()\n",
       "        )\n",
       "        (2): Conv2dNormActivation(\n",
       "          (0): ConvBn2d(\n",
       "            64, 24, kernel_size=(1, 1), stride=(1, 1), bias=False\n",
       "            (bn): BatchNorm2d(24, eps=0.001, momentum=0.01, affine=True, track_running_stats=True)\n",
       "            (weight_fake_quant): FusedMovingAvgObsFakeQuantize(\n",
       "              fake_quant_enabled=tensor([1]), observer_enabled=tensor([1]), scale=tensor([1.]), zero_point=tensor([0], dtype=torch.int32), dtype=torch.qint8, quant_min=-128, quant_max=127, qscheme=torch.per_channel_symmetric, reduce_range=False\n",
       "              (activation_post_process): MovingAveragePerChannelMinMaxObserver(min_val=tensor([]), max_val=tensor([]))\n",
       "            )\n",
       "            (activation_post_process): FusedMovingAvgObsFakeQuantize(\n",
       "              fake_quant_enabled=tensor([1]), observer_enabled=tensor([1]), scale=tensor([1.]), zero_point=tensor([0], dtype=torch.int32), dtype=torch.quint8, quant_min=0, quant_max=127, qscheme=torch.per_tensor_affine, reduce_range=True\n",
       "              (activation_post_process): MovingAverageMinMaxObserver(min_val=inf, max_val=-inf)\n",
       "            )\n",
       "          )\n",
       "          (1): Identity()\n",
       "        )\n",
       "      )\n",
       "      (skip_add): FloatFunctional(\n",
       "        (activation_post_process): FusedMovingAvgObsFakeQuantize(\n",
       "          fake_quant_enabled=tensor([1]), observer_enabled=tensor([1]), scale=tensor([1.]), zero_point=tensor([0], dtype=torch.int32), dtype=torch.quint8, quant_min=0, quant_max=127, qscheme=torch.per_tensor_affine, reduce_range=True\n",
       "          (activation_post_process): MovingAverageMinMaxObserver(min_val=inf, max_val=-inf)\n",
       "        )\n",
       "      )\n",
       "    )\n",
       "    (3): QuantizableInvertedResidual(\n",
       "      (block): Sequential(\n",
       "        (0): Conv2dNormActivation(\n",
       "          (0): ConvBnReLU2d(\n",
       "            24, 72, kernel_size=(1, 1), stride=(1, 1), bias=False\n",
       "            (bn): BatchNorm2d(72, eps=0.001, momentum=0.01, affine=True, track_running_stats=True)\n",
       "            (weight_fake_quant): FusedMovingAvgObsFakeQuantize(\n",
       "              fake_quant_enabled=tensor([1]), observer_enabled=tensor([1]), scale=tensor([1.]), zero_point=tensor([0], dtype=torch.int32), dtype=torch.qint8, quant_min=-128, quant_max=127, qscheme=torch.per_channel_symmetric, reduce_range=False\n",
       "              (activation_post_process): MovingAveragePerChannelMinMaxObserver(min_val=tensor([]), max_val=tensor([]))\n",
       "            )\n",
       "            (activation_post_process): FusedMovingAvgObsFakeQuantize(\n",
       "              fake_quant_enabled=tensor([1]), observer_enabled=tensor([1]), scale=tensor([1.]), zero_point=tensor([0], dtype=torch.int32), dtype=torch.quint8, quant_min=0, quant_max=127, qscheme=torch.per_tensor_affine, reduce_range=True\n",
       "              (activation_post_process): MovingAverageMinMaxObserver(min_val=inf, max_val=-inf)\n",
       "            )\n",
       "          )\n",
       "          (1): Identity()\n",
       "          (2): Identity()\n",
       "        )\n",
       "        (1): Conv2dNormActivation(\n",
       "          (0): ConvBnReLU2d(\n",
       "            72, 72, kernel_size=(3, 3), stride=(1, 1), padding=(1, 1), groups=72, bias=False\n",
       "            (bn): BatchNorm2d(72, eps=0.001, momentum=0.01, affine=True, track_running_stats=True)\n",
       "            (weight_fake_quant): FusedMovingAvgObsFakeQuantize(\n",
       "              fake_quant_enabled=tensor([1]), observer_enabled=tensor([1]), scale=tensor([1.]), zero_point=tensor([0], dtype=torch.int32), dtype=torch.qint8, quant_min=-128, quant_max=127, qscheme=torch.per_channel_symmetric, reduce_range=False\n",
       "              (activation_post_process): MovingAveragePerChannelMinMaxObserver(min_val=tensor([]), max_val=tensor([]))\n",
       "            )\n",
       "            (activation_post_process): FusedMovingAvgObsFakeQuantize(\n",
       "              fake_quant_enabled=tensor([1]), observer_enabled=tensor([1]), scale=tensor([1.]), zero_point=tensor([0], dtype=torch.int32), dtype=torch.quint8, quant_min=0, quant_max=127, qscheme=torch.per_tensor_affine, reduce_range=True\n",
       "              (activation_post_process): MovingAverageMinMaxObserver(min_val=inf, max_val=-inf)\n",
       "            )\n",
       "          )\n",
       "          (1): Identity()\n",
       "          (2): Identity()\n",
       "        )\n",
       "        (2): Conv2dNormActivation(\n",
       "          (0): ConvBn2d(\n",
       "            72, 24, kernel_size=(1, 1), stride=(1, 1), bias=False\n",
       "            (bn): BatchNorm2d(24, eps=0.001, momentum=0.01, affine=True, track_running_stats=True)\n",
       "            (weight_fake_quant): FusedMovingAvgObsFakeQuantize(\n",
       "              fake_quant_enabled=tensor([1]), observer_enabled=tensor([1]), scale=tensor([1.]), zero_point=tensor([0], dtype=torch.int32), dtype=torch.qint8, quant_min=-128, quant_max=127, qscheme=torch.per_channel_symmetric, reduce_range=False\n",
       "              (activation_post_process): MovingAveragePerChannelMinMaxObserver(min_val=tensor([]), max_val=tensor([]))\n",
       "            )\n",
       "            (activation_post_process): FusedMovingAvgObsFakeQuantize(\n",
       "              fake_quant_enabled=tensor([1]), observer_enabled=tensor([1]), scale=tensor([1.]), zero_point=tensor([0], dtype=torch.int32), dtype=torch.quint8, quant_min=0, quant_max=127, qscheme=torch.per_tensor_affine, reduce_range=True\n",
       "              (activation_post_process): MovingAverageMinMaxObserver(min_val=inf, max_val=-inf)\n",
       "            )\n",
       "          )\n",
       "          (1): Identity()\n",
       "        )\n",
       "      )\n",
       "      (skip_add): FloatFunctional(\n",
       "        (activation_post_process): FusedMovingAvgObsFakeQuantize(\n",
       "          fake_quant_enabled=tensor([1]), observer_enabled=tensor([1]), scale=tensor([1.]), zero_point=tensor([0], dtype=torch.int32), dtype=torch.quint8, quant_min=0, quant_max=127, qscheme=torch.per_tensor_affine, reduce_range=True\n",
       "          (activation_post_process): MovingAverageMinMaxObserver(min_val=inf, max_val=-inf)\n",
       "        )\n",
       "      )\n",
       "    )\n",
       "    (4): QuantizableInvertedResidual(\n",
       "      (block): Sequential(\n",
       "        (0): Conv2dNormActivation(\n",
       "          (0): ConvBnReLU2d(\n",
       "            24, 72, kernel_size=(1, 1), stride=(1, 1), bias=False\n",
       "            (bn): BatchNorm2d(72, eps=0.001, momentum=0.01, affine=True, track_running_stats=True)\n",
       "            (weight_fake_quant): FusedMovingAvgObsFakeQuantize(\n",
       "              fake_quant_enabled=tensor([1]), observer_enabled=tensor([1]), scale=tensor([1.]), zero_point=tensor([0], dtype=torch.int32), dtype=torch.qint8, quant_min=-128, quant_max=127, qscheme=torch.per_channel_symmetric, reduce_range=False\n",
       "              (activation_post_process): MovingAveragePerChannelMinMaxObserver(min_val=tensor([]), max_val=tensor([]))\n",
       "            )\n",
       "            (activation_post_process): FusedMovingAvgObsFakeQuantize(\n",
       "              fake_quant_enabled=tensor([1]), observer_enabled=tensor([1]), scale=tensor([1.]), zero_point=tensor([0], dtype=torch.int32), dtype=torch.quint8, quant_min=0, quant_max=127, qscheme=torch.per_tensor_affine, reduce_range=True\n",
       "              (activation_post_process): MovingAverageMinMaxObserver(min_val=inf, max_val=-inf)\n",
       "            )\n",
       "          )\n",
       "          (1): Identity()\n",
       "          (2): Identity()\n",
       "        )\n",
       "        (1): Conv2dNormActivation(\n",
       "          (0): ConvBnReLU2d(\n",
       "            72, 72, kernel_size=(5, 5), stride=(2, 2), padding=(2, 2), groups=72, bias=False\n",
       "            (bn): BatchNorm2d(72, eps=0.001, momentum=0.01, affine=True, track_running_stats=True)\n",
       "            (weight_fake_quant): FusedMovingAvgObsFakeQuantize(\n",
       "              fake_quant_enabled=tensor([1]), observer_enabled=tensor([1]), scale=tensor([1.]), zero_point=tensor([0], dtype=torch.int32), dtype=torch.qint8, quant_min=-128, quant_max=127, qscheme=torch.per_channel_symmetric, reduce_range=False\n",
       "              (activation_post_process): MovingAveragePerChannelMinMaxObserver(min_val=tensor([]), max_val=tensor([]))\n",
       "            )\n",
       "            (activation_post_process): FusedMovingAvgObsFakeQuantize(\n",
       "              fake_quant_enabled=tensor([1]), observer_enabled=tensor([1]), scale=tensor([1.]), zero_point=tensor([0], dtype=torch.int32), dtype=torch.quint8, quant_min=0, quant_max=127, qscheme=torch.per_tensor_affine, reduce_range=True\n",
       "              (activation_post_process): MovingAverageMinMaxObserver(min_val=inf, max_val=-inf)\n",
       "            )\n",
       "          )\n",
       "          (1): Identity()\n",
       "          (2): Identity()\n",
       "        )\n",
       "        (2): QuantizableSqueezeExcitation(\n",
       "          (avgpool): AdaptiveAvgPool2d(output_size=1)\n",
       "          (fc1): ConvReLU2d(\n",
       "            72, 24, kernel_size=(1, 1), stride=(1, 1)\n",
       "            (weight_fake_quant): FusedMovingAvgObsFakeQuantize(\n",
       "              fake_quant_enabled=tensor([1]), observer_enabled=tensor([1]), scale=tensor([1.]), zero_point=tensor([0], dtype=torch.int32), dtype=torch.qint8, quant_min=-128, quant_max=127, qscheme=torch.per_channel_symmetric, reduce_range=False\n",
       "              (activation_post_process): MovingAveragePerChannelMinMaxObserver(min_val=tensor([]), max_val=tensor([]))\n",
       "            )\n",
       "            (activation_post_process): FusedMovingAvgObsFakeQuantize(\n",
       "              fake_quant_enabled=tensor([1]), observer_enabled=tensor([1]), scale=tensor([1.]), zero_point=tensor([0], dtype=torch.int32), dtype=torch.quint8, quant_min=0, quant_max=127, qscheme=torch.per_tensor_affine, reduce_range=True\n",
       "              (activation_post_process): MovingAverageMinMaxObserver(min_val=inf, max_val=-inf)\n",
       "            )\n",
       "          )\n",
       "          (fc2): Conv2d(\n",
       "            24, 72, kernel_size=(1, 1), stride=(1, 1)\n",
       "            (weight_fake_quant): FusedMovingAvgObsFakeQuantize(\n",
       "              fake_quant_enabled=tensor([1]), observer_enabled=tensor([1]), scale=tensor([1.]), zero_point=tensor([0], dtype=torch.int32), dtype=torch.qint8, quant_min=-128, quant_max=127, qscheme=torch.per_channel_symmetric, reduce_range=False\n",
       "              (activation_post_process): MovingAveragePerChannelMinMaxObserver(min_val=tensor([]), max_val=tensor([]))\n",
       "            )\n",
       "            (activation_post_process): FusedMovingAvgObsFakeQuantize(\n",
       "              fake_quant_enabled=tensor([1]), observer_enabled=tensor([1]), scale=tensor([1.]), zero_point=tensor([0], dtype=torch.int32), dtype=torch.quint8, quant_min=0, quant_max=127, qscheme=torch.per_tensor_affine, reduce_range=True\n",
       "              (activation_post_process): MovingAverageMinMaxObserver(min_val=inf, max_val=-inf)\n",
       "            )\n",
       "          )\n",
       "          (activation): Identity()\n",
       "          (scale_activation): Hardsigmoid(\n",
       "            (activation_post_process): FixedQParamsFakeQuantize(\n",
       "              fake_quant_enabled=tensor([1], dtype=torch.uint8), observer_enabled=tensor([1], dtype=torch.uint8), scale=tensor([0.0039]), zero_point=tensor([0], dtype=torch.int32), dtype=torch.quint8, quant_min=0, quant_max=255, qscheme=torch.per_tensor_affine\n",
       "              (activation_post_process): FixedQParamsObserver()\n",
       "            )\n",
       "          )\n",
       "          (skip_mul): FloatFunctional(\n",
       "            (activation_post_process): FusedMovingAvgObsFakeQuantize(\n",
       "              fake_quant_enabled=tensor([1]), observer_enabled=tensor([1]), scale=tensor([1.]), zero_point=tensor([0], dtype=torch.int32), dtype=torch.quint8, quant_min=0, quant_max=127, qscheme=torch.per_tensor_affine, reduce_range=True\n",
       "              (activation_post_process): MovingAverageMinMaxObserver(min_val=inf, max_val=-inf)\n",
       "            )\n",
       "          )\n",
       "        )\n",
       "        (3): Conv2dNormActivation(\n",
       "          (0): ConvBn2d(\n",
       "            72, 40, kernel_size=(1, 1), stride=(1, 1), bias=False\n",
       "            (bn): BatchNorm2d(40, eps=0.001, momentum=0.01, affine=True, track_running_stats=True)\n",
       "            (weight_fake_quant): FusedMovingAvgObsFakeQuantize(\n",
       "              fake_quant_enabled=tensor([1]), observer_enabled=tensor([1]), scale=tensor([1.]), zero_point=tensor([0], dtype=torch.int32), dtype=torch.qint8, quant_min=-128, quant_max=127, qscheme=torch.per_channel_symmetric, reduce_range=False\n",
       "              (activation_post_process): MovingAveragePerChannelMinMaxObserver(min_val=tensor([]), max_val=tensor([]))\n",
       "            )\n",
       "            (activation_post_process): FusedMovingAvgObsFakeQuantize(\n",
       "              fake_quant_enabled=tensor([1]), observer_enabled=tensor([1]), scale=tensor([1.]), zero_point=tensor([0], dtype=torch.int32), dtype=torch.quint8, quant_min=0, quant_max=127, qscheme=torch.per_tensor_affine, reduce_range=True\n",
       "              (activation_post_process): MovingAverageMinMaxObserver(min_val=inf, max_val=-inf)\n",
       "            )\n",
       "          )\n",
       "          (1): Identity()\n",
       "        )\n",
       "      )\n",
       "      (skip_add): FloatFunctional(\n",
       "        (activation_post_process): FusedMovingAvgObsFakeQuantize(\n",
       "          fake_quant_enabled=tensor([1]), observer_enabled=tensor([1]), scale=tensor([1.]), zero_point=tensor([0], dtype=torch.int32), dtype=torch.quint8, quant_min=0, quant_max=127, qscheme=torch.per_tensor_affine, reduce_range=True\n",
       "          (activation_post_process): MovingAverageMinMaxObserver(min_val=inf, max_val=-inf)\n",
       "        )\n",
       "      )\n",
       "    )\n",
       "    (5): QuantizableInvertedResidual(\n",
       "      (block): Sequential(\n",
       "        (0): Conv2dNormActivation(\n",
       "          (0): ConvBnReLU2d(\n",
       "            40, 120, kernel_size=(1, 1), stride=(1, 1), bias=False\n",
       "            (bn): BatchNorm2d(120, eps=0.001, momentum=0.01, affine=True, track_running_stats=True)\n",
       "            (weight_fake_quant): FusedMovingAvgObsFakeQuantize(\n",
       "              fake_quant_enabled=tensor([1]), observer_enabled=tensor([1]), scale=tensor([1.]), zero_point=tensor([0], dtype=torch.int32), dtype=torch.qint8, quant_min=-128, quant_max=127, qscheme=torch.per_channel_symmetric, reduce_range=False\n",
       "              (activation_post_process): MovingAveragePerChannelMinMaxObserver(min_val=tensor([]), max_val=tensor([]))\n",
       "            )\n",
       "            (activation_post_process): FusedMovingAvgObsFakeQuantize(\n",
       "              fake_quant_enabled=tensor([1]), observer_enabled=tensor([1]), scale=tensor([1.]), zero_point=tensor([0], dtype=torch.int32), dtype=torch.quint8, quant_min=0, quant_max=127, qscheme=torch.per_tensor_affine, reduce_range=True\n",
       "              (activation_post_process): MovingAverageMinMaxObserver(min_val=inf, max_val=-inf)\n",
       "            )\n",
       "          )\n",
       "          (1): Identity()\n",
       "          (2): Identity()\n",
       "        )\n",
       "        (1): Conv2dNormActivation(\n",
       "          (0): ConvBnReLU2d(\n",
       "            120, 120, kernel_size=(5, 5), stride=(1, 1), padding=(2, 2), groups=120, bias=False\n",
       "            (bn): BatchNorm2d(120, eps=0.001, momentum=0.01, affine=True, track_running_stats=True)\n",
       "            (weight_fake_quant): FusedMovingAvgObsFakeQuantize(\n",
       "              fake_quant_enabled=tensor([1]), observer_enabled=tensor([1]), scale=tensor([1.]), zero_point=tensor([0], dtype=torch.int32), dtype=torch.qint8, quant_min=-128, quant_max=127, qscheme=torch.per_channel_symmetric, reduce_range=False\n",
       "              (activation_post_process): MovingAveragePerChannelMinMaxObserver(min_val=tensor([]), max_val=tensor([]))\n",
       "            )\n",
       "            (activation_post_process): FusedMovingAvgObsFakeQuantize(\n",
       "              fake_quant_enabled=tensor([1]), observer_enabled=tensor([1]), scale=tensor([1.]), zero_point=tensor([0], dtype=torch.int32), dtype=torch.quint8, quant_min=0, quant_max=127, qscheme=torch.per_tensor_affine, reduce_range=True\n",
       "              (activation_post_process): MovingAverageMinMaxObserver(min_val=inf, max_val=-inf)\n",
       "            )\n",
       "          )\n",
       "          (1): Identity()\n",
       "          (2): Identity()\n",
       "        )\n",
       "        (2): QuantizableSqueezeExcitation(\n",
       "          (avgpool): AdaptiveAvgPool2d(output_size=1)\n",
       "          (fc1): ConvReLU2d(\n",
       "            120, 32, kernel_size=(1, 1), stride=(1, 1)\n",
       "            (weight_fake_quant): FusedMovingAvgObsFakeQuantize(\n",
       "              fake_quant_enabled=tensor([1]), observer_enabled=tensor([1]), scale=tensor([1.]), zero_point=tensor([0], dtype=torch.int32), dtype=torch.qint8, quant_min=-128, quant_max=127, qscheme=torch.per_channel_symmetric, reduce_range=False\n",
       "              (activation_post_process): MovingAveragePerChannelMinMaxObserver(min_val=tensor([]), max_val=tensor([]))\n",
       "            )\n",
       "            (activation_post_process): FusedMovingAvgObsFakeQuantize(\n",
       "              fake_quant_enabled=tensor([1]), observer_enabled=tensor([1]), scale=tensor([1.]), zero_point=tensor([0], dtype=torch.int32), dtype=torch.quint8, quant_min=0, quant_max=127, qscheme=torch.per_tensor_affine, reduce_range=True\n",
       "              (activation_post_process): MovingAverageMinMaxObserver(min_val=inf, max_val=-inf)\n",
       "            )\n",
       "          )\n",
       "          (fc2): Conv2d(\n",
       "            32, 120, kernel_size=(1, 1), stride=(1, 1)\n",
       "            (weight_fake_quant): FusedMovingAvgObsFakeQuantize(\n",
       "              fake_quant_enabled=tensor([1]), observer_enabled=tensor([1]), scale=tensor([1.]), zero_point=tensor([0], dtype=torch.int32), dtype=torch.qint8, quant_min=-128, quant_max=127, qscheme=torch.per_channel_symmetric, reduce_range=False\n",
       "              (activation_post_process): MovingAveragePerChannelMinMaxObserver(min_val=tensor([]), max_val=tensor([]))\n",
       "            )\n",
       "            (activation_post_process): FusedMovingAvgObsFakeQuantize(\n",
       "              fake_quant_enabled=tensor([1]), observer_enabled=tensor([1]), scale=tensor([1.]), zero_point=tensor([0], dtype=torch.int32), dtype=torch.quint8, quant_min=0, quant_max=127, qscheme=torch.per_tensor_affine, reduce_range=True\n",
       "              (activation_post_process): MovingAverageMinMaxObserver(min_val=inf, max_val=-inf)\n",
       "            )\n",
       "          )\n",
       "          (activation): Identity()\n",
       "          (scale_activation): Hardsigmoid(\n",
       "            (activation_post_process): FixedQParamsFakeQuantize(\n",
       "              fake_quant_enabled=tensor([1], dtype=torch.uint8), observer_enabled=tensor([1], dtype=torch.uint8), scale=tensor([0.0039]), zero_point=tensor([0], dtype=torch.int32), dtype=torch.quint8, quant_min=0, quant_max=255, qscheme=torch.per_tensor_affine\n",
       "              (activation_post_process): FixedQParamsObserver()\n",
       "            )\n",
       "          )\n",
       "          (skip_mul): FloatFunctional(\n",
       "            (activation_post_process): FusedMovingAvgObsFakeQuantize(\n",
       "              fake_quant_enabled=tensor([1]), observer_enabled=tensor([1]), scale=tensor([1.]), zero_point=tensor([0], dtype=torch.int32), dtype=torch.quint8, quant_min=0, quant_max=127, qscheme=torch.per_tensor_affine, reduce_range=True\n",
       "              (activation_post_process): MovingAverageMinMaxObserver(min_val=inf, max_val=-inf)\n",
       "            )\n",
       "          )\n",
       "        )\n",
       "        (3): Conv2dNormActivation(\n",
       "          (0): ConvBn2d(\n",
       "            120, 40, kernel_size=(1, 1), stride=(1, 1), bias=False\n",
       "            (bn): BatchNorm2d(40, eps=0.001, momentum=0.01, affine=True, track_running_stats=True)\n",
       "            (weight_fake_quant): FusedMovingAvgObsFakeQuantize(\n",
       "              fake_quant_enabled=tensor([1]), observer_enabled=tensor([1]), scale=tensor([1.]), zero_point=tensor([0], dtype=torch.int32), dtype=torch.qint8, quant_min=-128, quant_max=127, qscheme=torch.per_channel_symmetric, reduce_range=False\n",
       "              (activation_post_process): MovingAveragePerChannelMinMaxObserver(min_val=tensor([]), max_val=tensor([]))\n",
       "            )\n",
       "            (activation_post_process): FusedMovingAvgObsFakeQuantize(\n",
       "              fake_quant_enabled=tensor([1]), observer_enabled=tensor([1]), scale=tensor([1.]), zero_point=tensor([0], dtype=torch.int32), dtype=torch.quint8, quant_min=0, quant_max=127, qscheme=torch.per_tensor_affine, reduce_range=True\n",
       "              (activation_post_process): MovingAverageMinMaxObserver(min_val=inf, max_val=-inf)\n",
       "            )\n",
       "          )\n",
       "          (1): Identity()\n",
       "        )\n",
       "      )\n",
       "      (skip_add): FloatFunctional(\n",
       "        (activation_post_process): FusedMovingAvgObsFakeQuantize(\n",
       "          fake_quant_enabled=tensor([1]), observer_enabled=tensor([1]), scale=tensor([1.]), zero_point=tensor([0], dtype=torch.int32), dtype=torch.quint8, quant_min=0, quant_max=127, qscheme=torch.per_tensor_affine, reduce_range=True\n",
       "          (activation_post_process): MovingAverageMinMaxObserver(min_val=inf, max_val=-inf)\n",
       "        )\n",
       "      )\n",
       "    )\n",
       "    (6): QuantizableInvertedResidual(\n",
       "      (block): Sequential(\n",
       "        (0): Conv2dNormActivation(\n",
       "          (0): ConvBnReLU2d(\n",
       "            40, 120, kernel_size=(1, 1), stride=(1, 1), bias=False\n",
       "            (bn): BatchNorm2d(120, eps=0.001, momentum=0.01, affine=True, track_running_stats=True)\n",
       "            (weight_fake_quant): FusedMovingAvgObsFakeQuantize(\n",
       "              fake_quant_enabled=tensor([1]), observer_enabled=tensor([1]), scale=tensor([1.]), zero_point=tensor([0], dtype=torch.int32), dtype=torch.qint8, quant_min=-128, quant_max=127, qscheme=torch.per_channel_symmetric, reduce_range=False\n",
       "              (activation_post_process): MovingAveragePerChannelMinMaxObserver(min_val=tensor([]), max_val=tensor([]))\n",
       "            )\n",
       "            (activation_post_process): FusedMovingAvgObsFakeQuantize(\n",
       "              fake_quant_enabled=tensor([1]), observer_enabled=tensor([1]), scale=tensor([1.]), zero_point=tensor([0], dtype=torch.int32), dtype=torch.quint8, quant_min=0, quant_max=127, qscheme=torch.per_tensor_affine, reduce_range=True\n",
       "              (activation_post_process): MovingAverageMinMaxObserver(min_val=inf, max_val=-inf)\n",
       "            )\n",
       "          )\n",
       "          (1): Identity()\n",
       "          (2): Identity()\n",
       "        )\n",
       "        (1): Conv2dNormActivation(\n",
       "          (0): ConvBnReLU2d(\n",
       "            120, 120, kernel_size=(5, 5), stride=(1, 1), padding=(2, 2), groups=120, bias=False\n",
       "            (bn): BatchNorm2d(120, eps=0.001, momentum=0.01, affine=True, track_running_stats=True)\n",
       "            (weight_fake_quant): FusedMovingAvgObsFakeQuantize(\n",
       "              fake_quant_enabled=tensor([1]), observer_enabled=tensor([1]), scale=tensor([1.]), zero_point=tensor([0], dtype=torch.int32), dtype=torch.qint8, quant_min=-128, quant_max=127, qscheme=torch.per_channel_symmetric, reduce_range=False\n",
       "              (activation_post_process): MovingAveragePerChannelMinMaxObserver(min_val=tensor([]), max_val=tensor([]))\n",
       "            )\n",
       "            (activation_post_process): FusedMovingAvgObsFakeQuantize(\n",
       "              fake_quant_enabled=tensor([1]), observer_enabled=tensor([1]), scale=tensor([1.]), zero_point=tensor([0], dtype=torch.int32), dtype=torch.quint8, quant_min=0, quant_max=127, qscheme=torch.per_tensor_affine, reduce_range=True\n",
       "              (activation_post_process): MovingAverageMinMaxObserver(min_val=inf, max_val=-inf)\n",
       "            )\n",
       "          )\n",
       "          (1): Identity()\n",
       "          (2): Identity()\n",
       "        )\n",
       "        (2): QuantizableSqueezeExcitation(\n",
       "          (avgpool): AdaptiveAvgPool2d(output_size=1)\n",
       "          (fc1): ConvReLU2d(\n",
       "            120, 32, kernel_size=(1, 1), stride=(1, 1)\n",
       "            (weight_fake_quant): FusedMovingAvgObsFakeQuantize(\n",
       "              fake_quant_enabled=tensor([1]), observer_enabled=tensor([1]), scale=tensor([1.]), zero_point=tensor([0], dtype=torch.int32), dtype=torch.qint8, quant_min=-128, quant_max=127, qscheme=torch.per_channel_symmetric, reduce_range=False\n",
       "              (activation_post_process): MovingAveragePerChannelMinMaxObserver(min_val=tensor([]), max_val=tensor([]))\n",
       "            )\n",
       "            (activation_post_process): FusedMovingAvgObsFakeQuantize(\n",
       "              fake_quant_enabled=tensor([1]), observer_enabled=tensor([1]), scale=tensor([1.]), zero_point=tensor([0], dtype=torch.int32), dtype=torch.quint8, quant_min=0, quant_max=127, qscheme=torch.per_tensor_affine, reduce_range=True\n",
       "              (activation_post_process): MovingAverageMinMaxObserver(min_val=inf, max_val=-inf)\n",
       "            )\n",
       "          )\n",
       "          (fc2): Conv2d(\n",
       "            32, 120, kernel_size=(1, 1), stride=(1, 1)\n",
       "            (weight_fake_quant): FusedMovingAvgObsFakeQuantize(\n",
       "              fake_quant_enabled=tensor([1]), observer_enabled=tensor([1]), scale=tensor([1.]), zero_point=tensor([0], dtype=torch.int32), dtype=torch.qint8, quant_min=-128, quant_max=127, qscheme=torch.per_channel_symmetric, reduce_range=False\n",
       "              (activation_post_process): MovingAveragePerChannelMinMaxObserver(min_val=tensor([]), max_val=tensor([]))\n",
       "            )\n",
       "            (activation_post_process): FusedMovingAvgObsFakeQuantize(\n",
       "              fake_quant_enabled=tensor([1]), observer_enabled=tensor([1]), scale=tensor([1.]), zero_point=tensor([0], dtype=torch.int32), dtype=torch.quint8, quant_min=0, quant_max=127, qscheme=torch.per_tensor_affine, reduce_range=True\n",
       "              (activation_post_process): MovingAverageMinMaxObserver(min_val=inf, max_val=-inf)\n",
       "            )\n",
       "          )\n",
       "          (activation): Identity()\n",
       "          (scale_activation): Hardsigmoid(\n",
       "            (activation_post_process): FixedQParamsFakeQuantize(\n",
       "              fake_quant_enabled=tensor([1], dtype=torch.uint8), observer_enabled=tensor([1], dtype=torch.uint8), scale=tensor([0.0039]), zero_point=tensor([0], dtype=torch.int32), dtype=torch.quint8, quant_min=0, quant_max=255, qscheme=torch.per_tensor_affine\n",
       "              (activation_post_process): FixedQParamsObserver()\n",
       "            )\n",
       "          )\n",
       "          (skip_mul): FloatFunctional(\n",
       "            (activation_post_process): FusedMovingAvgObsFakeQuantize(\n",
       "              fake_quant_enabled=tensor([1]), observer_enabled=tensor([1]), scale=tensor([1.]), zero_point=tensor([0], dtype=torch.int32), dtype=torch.quint8, quant_min=0, quant_max=127, qscheme=torch.per_tensor_affine, reduce_range=True\n",
       "              (activation_post_process): MovingAverageMinMaxObserver(min_val=inf, max_val=-inf)\n",
       "            )\n",
       "          )\n",
       "        )\n",
       "        (3): Conv2dNormActivation(\n",
       "          (0): ConvBn2d(\n",
       "            120, 40, kernel_size=(1, 1), stride=(1, 1), bias=False\n",
       "            (bn): BatchNorm2d(40, eps=0.001, momentum=0.01, affine=True, track_running_stats=True)\n",
       "            (weight_fake_quant): FusedMovingAvgObsFakeQuantize(\n",
       "              fake_quant_enabled=tensor([1]), observer_enabled=tensor([1]), scale=tensor([1.]), zero_point=tensor([0], dtype=torch.int32), dtype=torch.qint8, quant_min=-128, quant_max=127, qscheme=torch.per_channel_symmetric, reduce_range=False\n",
       "              (activation_post_process): MovingAveragePerChannelMinMaxObserver(min_val=tensor([]), max_val=tensor([]))\n",
       "            )\n",
       "            (activation_post_process): FusedMovingAvgObsFakeQuantize(\n",
       "              fake_quant_enabled=tensor([1]), observer_enabled=tensor([1]), scale=tensor([1.]), zero_point=tensor([0], dtype=torch.int32), dtype=torch.quint8, quant_min=0, quant_max=127, qscheme=torch.per_tensor_affine, reduce_range=True\n",
       "              (activation_post_process): MovingAverageMinMaxObserver(min_val=inf, max_val=-inf)\n",
       "            )\n",
       "          )\n",
       "          (1): Identity()\n",
       "        )\n",
       "      )\n",
       "      (skip_add): FloatFunctional(\n",
       "        (activation_post_process): FusedMovingAvgObsFakeQuantize(\n",
       "          fake_quant_enabled=tensor([1]), observer_enabled=tensor([1]), scale=tensor([1.]), zero_point=tensor([0], dtype=torch.int32), dtype=torch.quint8, quant_min=0, quant_max=127, qscheme=torch.per_tensor_affine, reduce_range=True\n",
       "          (activation_post_process): MovingAverageMinMaxObserver(min_val=inf, max_val=-inf)\n",
       "        )\n",
       "      )\n",
       "    )\n",
       "    (7): QuantizableInvertedResidual(\n",
       "      (block): Sequential(\n",
       "        (0): Conv2dNormActivation(\n",
       "          (0): ConvBn2d(\n",
       "            40, 240, kernel_size=(1, 1), stride=(1, 1), bias=False\n",
       "            (bn): BatchNorm2d(240, eps=0.001, momentum=0.01, affine=True, track_running_stats=True)\n",
       "            (weight_fake_quant): FusedMovingAvgObsFakeQuantize(\n",
       "              fake_quant_enabled=tensor([1]), observer_enabled=tensor([1]), scale=tensor([1.]), zero_point=tensor([0], dtype=torch.int32), dtype=torch.qint8, quant_min=-128, quant_max=127, qscheme=torch.per_channel_symmetric, reduce_range=False\n",
       "              (activation_post_process): MovingAveragePerChannelMinMaxObserver(min_val=tensor([]), max_val=tensor([]))\n",
       "            )\n",
       "            (activation_post_process): FusedMovingAvgObsFakeQuantize(\n",
       "              fake_quant_enabled=tensor([1]), observer_enabled=tensor([1]), scale=tensor([1.]), zero_point=tensor([0], dtype=torch.int32), dtype=torch.quint8, quant_min=0, quant_max=127, qscheme=torch.per_tensor_affine, reduce_range=True\n",
       "              (activation_post_process): MovingAverageMinMaxObserver(min_val=inf, max_val=-inf)\n",
       "            )\n",
       "          )\n",
       "          (1): Identity()\n",
       "          (2): Hardswish(\n",
       "            (activation_post_process): FusedMovingAvgObsFakeQuantize(\n",
       "              fake_quant_enabled=tensor([1]), observer_enabled=tensor([1]), scale=tensor([1.]), zero_point=tensor([0], dtype=torch.int32), dtype=torch.quint8, quant_min=0, quant_max=127, qscheme=torch.per_tensor_affine, reduce_range=True\n",
       "              (activation_post_process): MovingAverageMinMaxObserver(min_val=inf, max_val=-inf)\n",
       "            )\n",
       "          )\n",
       "        )\n",
       "        (1): Conv2dNormActivation(\n",
       "          (0): ConvBn2d(\n",
       "            240, 240, kernel_size=(3, 3), stride=(2, 2), padding=(1, 1), groups=240, bias=False\n",
       "            (bn): BatchNorm2d(240, eps=0.001, momentum=0.01, affine=True, track_running_stats=True)\n",
       "            (weight_fake_quant): FusedMovingAvgObsFakeQuantize(\n",
       "              fake_quant_enabled=tensor([1]), observer_enabled=tensor([1]), scale=tensor([1.]), zero_point=tensor([0], dtype=torch.int32), dtype=torch.qint8, quant_min=-128, quant_max=127, qscheme=torch.per_channel_symmetric, reduce_range=False\n",
       "              (activation_post_process): MovingAveragePerChannelMinMaxObserver(min_val=tensor([]), max_val=tensor([]))\n",
       "            )\n",
       "            (activation_post_process): FusedMovingAvgObsFakeQuantize(\n",
       "              fake_quant_enabled=tensor([1]), observer_enabled=tensor([1]), scale=tensor([1.]), zero_point=tensor([0], dtype=torch.int32), dtype=torch.quint8, quant_min=0, quant_max=127, qscheme=torch.per_tensor_affine, reduce_range=True\n",
       "              (activation_post_process): MovingAverageMinMaxObserver(min_val=inf, max_val=-inf)\n",
       "            )\n",
       "          )\n",
       "          (1): Identity()\n",
       "          (2): Hardswish(\n",
       "            (activation_post_process): FusedMovingAvgObsFakeQuantize(\n",
       "              fake_quant_enabled=tensor([1]), observer_enabled=tensor([1]), scale=tensor([1.]), zero_point=tensor([0], dtype=torch.int32), dtype=torch.quint8, quant_min=0, quant_max=127, qscheme=torch.per_tensor_affine, reduce_range=True\n",
       "              (activation_post_process): MovingAverageMinMaxObserver(min_val=inf, max_val=-inf)\n",
       "            )\n",
       "          )\n",
       "        )\n",
       "        (2): Conv2dNormActivation(\n",
       "          (0): ConvBn2d(\n",
       "            240, 80, kernel_size=(1, 1), stride=(1, 1), bias=False\n",
       "            (bn): BatchNorm2d(80, eps=0.001, momentum=0.01, affine=True, track_running_stats=True)\n",
       "            (weight_fake_quant): FusedMovingAvgObsFakeQuantize(\n",
       "              fake_quant_enabled=tensor([1]), observer_enabled=tensor([1]), scale=tensor([1.]), zero_point=tensor([0], dtype=torch.int32), dtype=torch.qint8, quant_min=-128, quant_max=127, qscheme=torch.per_channel_symmetric, reduce_range=False\n",
       "              (activation_post_process): MovingAveragePerChannelMinMaxObserver(min_val=tensor([]), max_val=tensor([]))\n",
       "            )\n",
       "            (activation_post_process): FusedMovingAvgObsFakeQuantize(\n",
       "              fake_quant_enabled=tensor([1]), observer_enabled=tensor([1]), scale=tensor([1.]), zero_point=tensor([0], dtype=torch.int32), dtype=torch.quint8, quant_min=0, quant_max=127, qscheme=torch.per_tensor_affine, reduce_range=True\n",
       "              (activation_post_process): MovingAverageMinMaxObserver(min_val=inf, max_val=-inf)\n",
       "            )\n",
       "          )\n",
       "          (1): Identity()\n",
       "        )\n",
       "      )\n",
       "      (skip_add): FloatFunctional(\n",
       "        (activation_post_process): FusedMovingAvgObsFakeQuantize(\n",
       "          fake_quant_enabled=tensor([1]), observer_enabled=tensor([1]), scale=tensor([1.]), zero_point=tensor([0], dtype=torch.int32), dtype=torch.quint8, quant_min=0, quant_max=127, qscheme=torch.per_tensor_affine, reduce_range=True\n",
       "          (activation_post_process): MovingAverageMinMaxObserver(min_val=inf, max_val=-inf)\n",
       "        )\n",
       "      )\n",
       "    )\n",
       "    (8): QuantizableInvertedResidual(\n",
       "      (block): Sequential(\n",
       "        (0): Conv2dNormActivation(\n",
       "          (0): ConvBn2d(\n",
       "            80, 200, kernel_size=(1, 1), stride=(1, 1), bias=False\n",
       "            (bn): BatchNorm2d(200, eps=0.001, momentum=0.01, affine=True, track_running_stats=True)\n",
       "            (weight_fake_quant): FusedMovingAvgObsFakeQuantize(\n",
       "              fake_quant_enabled=tensor([1]), observer_enabled=tensor([1]), scale=tensor([1.]), zero_point=tensor([0], dtype=torch.int32), dtype=torch.qint8, quant_min=-128, quant_max=127, qscheme=torch.per_channel_symmetric, reduce_range=False\n",
       "              (activation_post_process): MovingAveragePerChannelMinMaxObserver(min_val=tensor([]), max_val=tensor([]))\n",
       "            )\n",
       "            (activation_post_process): FusedMovingAvgObsFakeQuantize(\n",
       "              fake_quant_enabled=tensor([1]), observer_enabled=tensor([1]), scale=tensor([1.]), zero_point=tensor([0], dtype=torch.int32), dtype=torch.quint8, quant_min=0, quant_max=127, qscheme=torch.per_tensor_affine, reduce_range=True\n",
       "              (activation_post_process): MovingAverageMinMaxObserver(min_val=inf, max_val=-inf)\n",
       "            )\n",
       "          )\n",
       "          (1): Identity()\n",
       "          (2): Hardswish(\n",
       "            (activation_post_process): FusedMovingAvgObsFakeQuantize(\n",
       "              fake_quant_enabled=tensor([1]), observer_enabled=tensor([1]), scale=tensor([1.]), zero_point=tensor([0], dtype=torch.int32), dtype=torch.quint8, quant_min=0, quant_max=127, qscheme=torch.per_tensor_affine, reduce_range=True\n",
       "              (activation_post_process): MovingAverageMinMaxObserver(min_val=inf, max_val=-inf)\n",
       "            )\n",
       "          )\n",
       "        )\n",
       "        (1): Conv2dNormActivation(\n",
       "          (0): ConvBn2d(\n",
       "            200, 200, kernel_size=(3, 3), stride=(1, 1), padding=(1, 1), groups=200, bias=False\n",
       "            (bn): BatchNorm2d(200, eps=0.001, momentum=0.01, affine=True, track_running_stats=True)\n",
       "            (weight_fake_quant): FusedMovingAvgObsFakeQuantize(\n",
       "              fake_quant_enabled=tensor([1]), observer_enabled=tensor([1]), scale=tensor([1.]), zero_point=tensor([0], dtype=torch.int32), dtype=torch.qint8, quant_min=-128, quant_max=127, qscheme=torch.per_channel_symmetric, reduce_range=False\n",
       "              (activation_post_process): MovingAveragePerChannelMinMaxObserver(min_val=tensor([]), max_val=tensor([]))\n",
       "            )\n",
       "            (activation_post_process): FusedMovingAvgObsFakeQuantize(\n",
       "              fake_quant_enabled=tensor([1]), observer_enabled=tensor([1]), scale=tensor([1.]), zero_point=tensor([0], dtype=torch.int32), dtype=torch.quint8, quant_min=0, quant_max=127, qscheme=torch.per_tensor_affine, reduce_range=True\n",
       "              (activation_post_process): MovingAverageMinMaxObserver(min_val=inf, max_val=-inf)\n",
       "            )\n",
       "          )\n",
       "          (1): Identity()\n",
       "          (2): Hardswish(\n",
       "            (activation_post_process): FusedMovingAvgObsFakeQuantize(\n",
       "              fake_quant_enabled=tensor([1]), observer_enabled=tensor([1]), scale=tensor([1.]), zero_point=tensor([0], dtype=torch.int32), dtype=torch.quint8, quant_min=0, quant_max=127, qscheme=torch.per_tensor_affine, reduce_range=True\n",
       "              (activation_post_process): MovingAverageMinMaxObserver(min_val=inf, max_val=-inf)\n",
       "            )\n",
       "          )\n",
       "        )\n",
       "        (2): Conv2dNormActivation(\n",
       "          (0): ConvBn2d(\n",
       "            200, 80, kernel_size=(1, 1), stride=(1, 1), bias=False\n",
       "            (bn): BatchNorm2d(80, eps=0.001, momentum=0.01, affine=True, track_running_stats=True)\n",
       "            (weight_fake_quant): FusedMovingAvgObsFakeQuantize(\n",
       "              fake_quant_enabled=tensor([1]), observer_enabled=tensor([1]), scale=tensor([1.]), zero_point=tensor([0], dtype=torch.int32), dtype=torch.qint8, quant_min=-128, quant_max=127, qscheme=torch.per_channel_symmetric, reduce_range=False\n",
       "              (activation_post_process): MovingAveragePerChannelMinMaxObserver(min_val=tensor([]), max_val=tensor([]))\n",
       "            )\n",
       "            (activation_post_process): FusedMovingAvgObsFakeQuantize(\n",
       "              fake_quant_enabled=tensor([1]), observer_enabled=tensor([1]), scale=tensor([1.]), zero_point=tensor([0], dtype=torch.int32), dtype=torch.quint8, quant_min=0, quant_max=127, qscheme=torch.per_tensor_affine, reduce_range=True\n",
       "              (activation_post_process): MovingAverageMinMaxObserver(min_val=inf, max_val=-inf)\n",
       "            )\n",
       "          )\n",
       "          (1): Identity()\n",
       "        )\n",
       "      )\n",
       "      (skip_add): FloatFunctional(\n",
       "        (activation_post_process): FusedMovingAvgObsFakeQuantize(\n",
       "          fake_quant_enabled=tensor([1]), observer_enabled=tensor([1]), scale=tensor([1.]), zero_point=tensor([0], dtype=torch.int32), dtype=torch.quint8, quant_min=0, quant_max=127, qscheme=torch.per_tensor_affine, reduce_range=True\n",
       "          (activation_post_process): MovingAverageMinMaxObserver(min_val=inf, max_val=-inf)\n",
       "        )\n",
       "      )\n",
       "    )\n",
       "    (9): QuantizableInvertedResidual(\n",
       "      (block): Sequential(\n",
       "        (0): Conv2dNormActivation(\n",
       "          (0): ConvBn2d(\n",
       "            80, 184, kernel_size=(1, 1), stride=(1, 1), bias=False\n",
       "            (bn): BatchNorm2d(184, eps=0.001, momentum=0.01, affine=True, track_running_stats=True)\n",
       "            (weight_fake_quant): FusedMovingAvgObsFakeQuantize(\n",
       "              fake_quant_enabled=tensor([1]), observer_enabled=tensor([1]), scale=tensor([1.]), zero_point=tensor([0], dtype=torch.int32), dtype=torch.qint8, quant_min=-128, quant_max=127, qscheme=torch.per_channel_symmetric, reduce_range=False\n",
       "              (activation_post_process): MovingAveragePerChannelMinMaxObserver(min_val=tensor([]), max_val=tensor([]))\n",
       "            )\n",
       "            (activation_post_process): FusedMovingAvgObsFakeQuantize(\n",
       "              fake_quant_enabled=tensor([1]), observer_enabled=tensor([1]), scale=tensor([1.]), zero_point=tensor([0], dtype=torch.int32), dtype=torch.quint8, quant_min=0, quant_max=127, qscheme=torch.per_tensor_affine, reduce_range=True\n",
       "              (activation_post_process): MovingAverageMinMaxObserver(min_val=inf, max_val=-inf)\n",
       "            )\n",
       "          )\n",
       "          (1): Identity()\n",
       "          (2): Hardswish(\n",
       "            (activation_post_process): FusedMovingAvgObsFakeQuantize(\n",
       "              fake_quant_enabled=tensor([1]), observer_enabled=tensor([1]), scale=tensor([1.]), zero_point=tensor([0], dtype=torch.int32), dtype=torch.quint8, quant_min=0, quant_max=127, qscheme=torch.per_tensor_affine, reduce_range=True\n",
       "              (activation_post_process): MovingAverageMinMaxObserver(min_val=inf, max_val=-inf)\n",
       "            )\n",
       "          )\n",
       "        )\n",
       "        (1): Conv2dNormActivation(\n",
       "          (0): ConvBn2d(\n",
       "            184, 184, kernel_size=(3, 3), stride=(1, 1), padding=(1, 1), groups=184, bias=False\n",
       "            (bn): BatchNorm2d(184, eps=0.001, momentum=0.01, affine=True, track_running_stats=True)\n",
       "            (weight_fake_quant): FusedMovingAvgObsFakeQuantize(\n",
       "              fake_quant_enabled=tensor([1]), observer_enabled=tensor([1]), scale=tensor([1.]), zero_point=tensor([0], dtype=torch.int32), dtype=torch.qint8, quant_min=-128, quant_max=127, qscheme=torch.per_channel_symmetric, reduce_range=False\n",
       "              (activation_post_process): MovingAveragePerChannelMinMaxObserver(min_val=tensor([]), max_val=tensor([]))\n",
       "            )\n",
       "            (activation_post_process): FusedMovingAvgObsFakeQuantize(\n",
       "              fake_quant_enabled=tensor([1]), observer_enabled=tensor([1]), scale=tensor([1.]), zero_point=tensor([0], dtype=torch.int32), dtype=torch.quint8, quant_min=0, quant_max=127, qscheme=torch.per_tensor_affine, reduce_range=True\n",
       "              (activation_post_process): MovingAverageMinMaxObserver(min_val=inf, max_val=-inf)\n",
       "            )\n",
       "          )\n",
       "          (1): Identity()\n",
       "          (2): Hardswish(\n",
       "            (activation_post_process): FusedMovingAvgObsFakeQuantize(\n",
       "              fake_quant_enabled=tensor([1]), observer_enabled=tensor([1]), scale=tensor([1.]), zero_point=tensor([0], dtype=torch.int32), dtype=torch.quint8, quant_min=0, quant_max=127, qscheme=torch.per_tensor_affine, reduce_range=True\n",
       "              (activation_post_process): MovingAverageMinMaxObserver(min_val=inf, max_val=-inf)\n",
       "            )\n",
       "          )\n",
       "        )\n",
       "        (2): Conv2dNormActivation(\n",
       "          (0): ConvBn2d(\n",
       "            184, 80, kernel_size=(1, 1), stride=(1, 1), bias=False\n",
       "            (bn): BatchNorm2d(80, eps=0.001, momentum=0.01, affine=True, track_running_stats=True)\n",
       "            (weight_fake_quant): FusedMovingAvgObsFakeQuantize(\n",
       "              fake_quant_enabled=tensor([1]), observer_enabled=tensor([1]), scale=tensor([1.]), zero_point=tensor([0], dtype=torch.int32), dtype=torch.qint8, quant_min=-128, quant_max=127, qscheme=torch.per_channel_symmetric, reduce_range=False\n",
       "              (activation_post_process): MovingAveragePerChannelMinMaxObserver(min_val=tensor([]), max_val=tensor([]))\n",
       "            )\n",
       "            (activation_post_process): FusedMovingAvgObsFakeQuantize(\n",
       "              fake_quant_enabled=tensor([1]), observer_enabled=tensor([1]), scale=tensor([1.]), zero_point=tensor([0], dtype=torch.int32), dtype=torch.quint8, quant_min=0, quant_max=127, qscheme=torch.per_tensor_affine, reduce_range=True\n",
       "              (activation_post_process): MovingAverageMinMaxObserver(min_val=inf, max_val=-inf)\n",
       "            )\n",
       "          )\n",
       "          (1): Identity()\n",
       "        )\n",
       "      )\n",
       "      (skip_add): FloatFunctional(\n",
       "        (activation_post_process): FusedMovingAvgObsFakeQuantize(\n",
       "          fake_quant_enabled=tensor([1]), observer_enabled=tensor([1]), scale=tensor([1.]), zero_point=tensor([0], dtype=torch.int32), dtype=torch.quint8, quant_min=0, quant_max=127, qscheme=torch.per_tensor_affine, reduce_range=True\n",
       "          (activation_post_process): MovingAverageMinMaxObserver(min_val=inf, max_val=-inf)\n",
       "        )\n",
       "      )\n",
       "    )\n",
       "    (10): QuantizableInvertedResidual(\n",
       "      (block): Sequential(\n",
       "        (0): Conv2dNormActivation(\n",
       "          (0): ConvBn2d(\n",
       "            80, 184, kernel_size=(1, 1), stride=(1, 1), bias=False\n",
       "            (bn): BatchNorm2d(184, eps=0.001, momentum=0.01, affine=True, track_running_stats=True)\n",
       "            (weight_fake_quant): FusedMovingAvgObsFakeQuantize(\n",
       "              fake_quant_enabled=tensor([1]), observer_enabled=tensor([1]), scale=tensor([1.]), zero_point=tensor([0], dtype=torch.int32), dtype=torch.qint8, quant_min=-128, quant_max=127, qscheme=torch.per_channel_symmetric, reduce_range=False\n",
       "              (activation_post_process): MovingAveragePerChannelMinMaxObserver(min_val=tensor([]), max_val=tensor([]))\n",
       "            )\n",
       "            (activation_post_process): FusedMovingAvgObsFakeQuantize(\n",
       "              fake_quant_enabled=tensor([1]), observer_enabled=tensor([1]), scale=tensor([1.]), zero_point=tensor([0], dtype=torch.int32), dtype=torch.quint8, quant_min=0, quant_max=127, qscheme=torch.per_tensor_affine, reduce_range=True\n",
       "              (activation_post_process): MovingAverageMinMaxObserver(min_val=inf, max_val=-inf)\n",
       "            )\n",
       "          )\n",
       "          (1): Identity()\n",
       "          (2): Hardswish(\n",
       "            (activation_post_process): FusedMovingAvgObsFakeQuantize(\n",
       "              fake_quant_enabled=tensor([1]), observer_enabled=tensor([1]), scale=tensor([1.]), zero_point=tensor([0], dtype=torch.int32), dtype=torch.quint8, quant_min=0, quant_max=127, qscheme=torch.per_tensor_affine, reduce_range=True\n",
       "              (activation_post_process): MovingAverageMinMaxObserver(min_val=inf, max_val=-inf)\n",
       "            )\n",
       "          )\n",
       "        )\n",
       "        (1): Conv2dNormActivation(\n",
       "          (0): ConvBn2d(\n",
       "            184, 184, kernel_size=(3, 3), stride=(1, 1), padding=(1, 1), groups=184, bias=False\n",
       "            (bn): BatchNorm2d(184, eps=0.001, momentum=0.01, affine=True, track_running_stats=True)\n",
       "            (weight_fake_quant): FusedMovingAvgObsFakeQuantize(\n",
       "              fake_quant_enabled=tensor([1]), observer_enabled=tensor([1]), scale=tensor([1.]), zero_point=tensor([0], dtype=torch.int32), dtype=torch.qint8, quant_min=-128, quant_max=127, qscheme=torch.per_channel_symmetric, reduce_range=False\n",
       "              (activation_post_process): MovingAveragePerChannelMinMaxObserver(min_val=tensor([]), max_val=tensor([]))\n",
       "            )\n",
       "            (activation_post_process): FusedMovingAvgObsFakeQuantize(\n",
       "              fake_quant_enabled=tensor([1]), observer_enabled=tensor([1]), scale=tensor([1.]), zero_point=tensor([0], dtype=torch.int32), dtype=torch.quint8, quant_min=0, quant_max=127, qscheme=torch.per_tensor_affine, reduce_range=True\n",
       "              (activation_post_process): MovingAverageMinMaxObserver(min_val=inf, max_val=-inf)\n",
       "            )\n",
       "          )\n",
       "          (1): Identity()\n",
       "          (2): Hardswish(\n",
       "            (activation_post_process): FusedMovingAvgObsFakeQuantize(\n",
       "              fake_quant_enabled=tensor([1]), observer_enabled=tensor([1]), scale=tensor([1.]), zero_point=tensor([0], dtype=torch.int32), dtype=torch.quint8, quant_min=0, quant_max=127, qscheme=torch.per_tensor_affine, reduce_range=True\n",
       "              (activation_post_process): MovingAverageMinMaxObserver(min_val=inf, max_val=-inf)\n",
       "            )\n",
       "          )\n",
       "        )\n",
       "        (2): Conv2dNormActivation(\n",
       "          (0): ConvBn2d(\n",
       "            184, 80, kernel_size=(1, 1), stride=(1, 1), bias=False\n",
       "            (bn): BatchNorm2d(80, eps=0.001, momentum=0.01, affine=True, track_running_stats=True)\n",
       "            (weight_fake_quant): FusedMovingAvgObsFakeQuantize(\n",
       "              fake_quant_enabled=tensor([1]), observer_enabled=tensor([1]), scale=tensor([1.]), zero_point=tensor([0], dtype=torch.int32), dtype=torch.qint8, quant_min=-128, quant_max=127, qscheme=torch.per_channel_symmetric, reduce_range=False\n",
       "              (activation_post_process): MovingAveragePerChannelMinMaxObserver(min_val=tensor([]), max_val=tensor([]))\n",
       "            )\n",
       "            (activation_post_process): FusedMovingAvgObsFakeQuantize(\n",
       "              fake_quant_enabled=tensor([1]), observer_enabled=tensor([1]), scale=tensor([1.]), zero_point=tensor([0], dtype=torch.int32), dtype=torch.quint8, quant_min=0, quant_max=127, qscheme=torch.per_tensor_affine, reduce_range=True\n",
       "              (activation_post_process): MovingAverageMinMaxObserver(min_val=inf, max_val=-inf)\n",
       "            )\n",
       "          )\n",
       "          (1): Identity()\n",
       "        )\n",
       "      )\n",
       "      (skip_add): FloatFunctional(\n",
       "        (activation_post_process): FusedMovingAvgObsFakeQuantize(\n",
       "          fake_quant_enabled=tensor([1]), observer_enabled=tensor([1]), scale=tensor([1.]), zero_point=tensor([0], dtype=torch.int32), dtype=torch.quint8, quant_min=0, quant_max=127, qscheme=torch.per_tensor_affine, reduce_range=True\n",
       "          (activation_post_process): MovingAverageMinMaxObserver(min_val=inf, max_val=-inf)\n",
       "        )\n",
       "      )\n",
       "    )\n",
       "    (11): QuantizableInvertedResidual(\n",
       "      (block): Sequential(\n",
       "        (0): Conv2dNormActivation(\n",
       "          (0): ConvBn2d(\n",
       "            80, 480, kernel_size=(1, 1), stride=(1, 1), bias=False\n",
       "            (bn): BatchNorm2d(480, eps=0.001, momentum=0.01, affine=True, track_running_stats=True)\n",
       "            (weight_fake_quant): FusedMovingAvgObsFakeQuantize(\n",
       "              fake_quant_enabled=tensor([1]), observer_enabled=tensor([1]), scale=tensor([1.]), zero_point=tensor([0], dtype=torch.int32), dtype=torch.qint8, quant_min=-128, quant_max=127, qscheme=torch.per_channel_symmetric, reduce_range=False\n",
       "              (activation_post_process): MovingAveragePerChannelMinMaxObserver(min_val=tensor([]), max_val=tensor([]))\n",
       "            )\n",
       "            (activation_post_process): FusedMovingAvgObsFakeQuantize(\n",
       "              fake_quant_enabled=tensor([1]), observer_enabled=tensor([1]), scale=tensor([1.]), zero_point=tensor([0], dtype=torch.int32), dtype=torch.quint8, quant_min=0, quant_max=127, qscheme=torch.per_tensor_affine, reduce_range=True\n",
       "              (activation_post_process): MovingAverageMinMaxObserver(min_val=inf, max_val=-inf)\n",
       "            )\n",
       "          )\n",
       "          (1): Identity()\n",
       "          (2): Hardswish(\n",
       "            (activation_post_process): FusedMovingAvgObsFakeQuantize(\n",
       "              fake_quant_enabled=tensor([1]), observer_enabled=tensor([1]), scale=tensor([1.]), zero_point=tensor([0], dtype=torch.int32), dtype=torch.quint8, quant_min=0, quant_max=127, qscheme=torch.per_tensor_affine, reduce_range=True\n",
       "              (activation_post_process): MovingAverageMinMaxObserver(min_val=inf, max_val=-inf)\n",
       "            )\n",
       "          )\n",
       "        )\n",
       "        (1): Conv2dNormActivation(\n",
       "          (0): ConvBn2d(\n",
       "            480, 480, kernel_size=(3, 3), stride=(1, 1), padding=(1, 1), groups=480, bias=False\n",
       "            (bn): BatchNorm2d(480, eps=0.001, momentum=0.01, affine=True, track_running_stats=True)\n",
       "            (weight_fake_quant): FusedMovingAvgObsFakeQuantize(\n",
       "              fake_quant_enabled=tensor([1]), observer_enabled=tensor([1]), scale=tensor([1.]), zero_point=tensor([0], dtype=torch.int32), dtype=torch.qint8, quant_min=-128, quant_max=127, qscheme=torch.per_channel_symmetric, reduce_range=False\n",
       "              (activation_post_process): MovingAveragePerChannelMinMaxObserver(min_val=tensor([]), max_val=tensor([]))\n",
       "            )\n",
       "            (activation_post_process): FusedMovingAvgObsFakeQuantize(\n",
       "              fake_quant_enabled=tensor([1]), observer_enabled=tensor([1]), scale=tensor([1.]), zero_point=tensor([0], dtype=torch.int32), dtype=torch.quint8, quant_min=0, quant_max=127, qscheme=torch.per_tensor_affine, reduce_range=True\n",
       "              (activation_post_process): MovingAverageMinMaxObserver(min_val=inf, max_val=-inf)\n",
       "            )\n",
       "          )\n",
       "          (1): Identity()\n",
       "          (2): Hardswish(\n",
       "            (activation_post_process): FusedMovingAvgObsFakeQuantize(\n",
       "              fake_quant_enabled=tensor([1]), observer_enabled=tensor([1]), scale=tensor([1.]), zero_point=tensor([0], dtype=torch.int32), dtype=torch.quint8, quant_min=0, quant_max=127, qscheme=torch.per_tensor_affine, reduce_range=True\n",
       "              (activation_post_process): MovingAverageMinMaxObserver(min_val=inf, max_val=-inf)\n",
       "            )\n",
       "          )\n",
       "        )\n",
       "        (2): QuantizableSqueezeExcitation(\n",
       "          (avgpool): AdaptiveAvgPool2d(output_size=1)\n",
       "          (fc1): ConvReLU2d(\n",
       "            480, 120, kernel_size=(1, 1), stride=(1, 1)\n",
       "            (weight_fake_quant): FusedMovingAvgObsFakeQuantize(\n",
       "              fake_quant_enabled=tensor([1]), observer_enabled=tensor([1]), scale=tensor([1.]), zero_point=tensor([0], dtype=torch.int32), dtype=torch.qint8, quant_min=-128, quant_max=127, qscheme=torch.per_channel_symmetric, reduce_range=False\n",
       "              (activation_post_process): MovingAveragePerChannelMinMaxObserver(min_val=tensor([]), max_val=tensor([]))\n",
       "            )\n",
       "            (activation_post_process): FusedMovingAvgObsFakeQuantize(\n",
       "              fake_quant_enabled=tensor([1]), observer_enabled=tensor([1]), scale=tensor([1.]), zero_point=tensor([0], dtype=torch.int32), dtype=torch.quint8, quant_min=0, quant_max=127, qscheme=torch.per_tensor_affine, reduce_range=True\n",
       "              (activation_post_process): MovingAverageMinMaxObserver(min_val=inf, max_val=-inf)\n",
       "            )\n",
       "          )\n",
       "          (fc2): Conv2d(\n",
       "            120, 480, kernel_size=(1, 1), stride=(1, 1)\n",
       "            (weight_fake_quant): FusedMovingAvgObsFakeQuantize(\n",
       "              fake_quant_enabled=tensor([1]), observer_enabled=tensor([1]), scale=tensor([1.]), zero_point=tensor([0], dtype=torch.int32), dtype=torch.qint8, quant_min=-128, quant_max=127, qscheme=torch.per_channel_symmetric, reduce_range=False\n",
       "              (activation_post_process): MovingAveragePerChannelMinMaxObserver(min_val=tensor([]), max_val=tensor([]))\n",
       "            )\n",
       "            (activation_post_process): FusedMovingAvgObsFakeQuantize(\n",
       "              fake_quant_enabled=tensor([1]), observer_enabled=tensor([1]), scale=tensor([1.]), zero_point=tensor([0], dtype=torch.int32), dtype=torch.quint8, quant_min=0, quant_max=127, qscheme=torch.per_tensor_affine, reduce_range=True\n",
       "              (activation_post_process): MovingAverageMinMaxObserver(min_val=inf, max_val=-inf)\n",
       "            )\n",
       "          )\n",
       "          (activation): Identity()\n",
       "          (scale_activation): Hardsigmoid(\n",
       "            (activation_post_process): FixedQParamsFakeQuantize(\n",
       "              fake_quant_enabled=tensor([1], dtype=torch.uint8), observer_enabled=tensor([1], dtype=torch.uint8), scale=tensor([0.0039]), zero_point=tensor([0], dtype=torch.int32), dtype=torch.quint8, quant_min=0, quant_max=255, qscheme=torch.per_tensor_affine\n",
       "              (activation_post_process): FixedQParamsObserver()\n",
       "            )\n",
       "          )\n",
       "          (skip_mul): FloatFunctional(\n",
       "            (activation_post_process): FusedMovingAvgObsFakeQuantize(\n",
       "              fake_quant_enabled=tensor([1]), observer_enabled=tensor([1]), scale=tensor([1.]), zero_point=tensor([0], dtype=torch.int32), dtype=torch.quint8, quant_min=0, quant_max=127, qscheme=torch.per_tensor_affine, reduce_range=True\n",
       "              (activation_post_process): MovingAverageMinMaxObserver(min_val=inf, max_val=-inf)\n",
       "            )\n",
       "          )\n",
       "        )\n",
       "        (3): Conv2dNormActivation(\n",
       "          (0): ConvBn2d(\n",
       "            480, 112, kernel_size=(1, 1), stride=(1, 1), bias=False\n",
       "            (bn): BatchNorm2d(112, eps=0.001, momentum=0.01, affine=True, track_running_stats=True)\n",
       "            (weight_fake_quant): FusedMovingAvgObsFakeQuantize(\n",
       "              fake_quant_enabled=tensor([1]), observer_enabled=tensor([1]), scale=tensor([1.]), zero_point=tensor([0], dtype=torch.int32), dtype=torch.qint8, quant_min=-128, quant_max=127, qscheme=torch.per_channel_symmetric, reduce_range=False\n",
       "              (activation_post_process): MovingAveragePerChannelMinMaxObserver(min_val=tensor([]), max_val=tensor([]))\n",
       "            )\n",
       "            (activation_post_process): FusedMovingAvgObsFakeQuantize(\n",
       "              fake_quant_enabled=tensor([1]), observer_enabled=tensor([1]), scale=tensor([1.]), zero_point=tensor([0], dtype=torch.int32), dtype=torch.quint8, quant_min=0, quant_max=127, qscheme=torch.per_tensor_affine, reduce_range=True\n",
       "              (activation_post_process): MovingAverageMinMaxObserver(min_val=inf, max_val=-inf)\n",
       "            )\n",
       "          )\n",
       "          (1): Identity()\n",
       "        )\n",
       "      )\n",
       "      (skip_add): FloatFunctional(\n",
       "        (activation_post_process): FusedMovingAvgObsFakeQuantize(\n",
       "          fake_quant_enabled=tensor([1]), observer_enabled=tensor([1]), scale=tensor([1.]), zero_point=tensor([0], dtype=torch.int32), dtype=torch.quint8, quant_min=0, quant_max=127, qscheme=torch.per_tensor_affine, reduce_range=True\n",
       "          (activation_post_process): MovingAverageMinMaxObserver(min_val=inf, max_val=-inf)\n",
       "        )\n",
       "      )\n",
       "    )\n",
       "    (12): QuantizableInvertedResidual(\n",
       "      (block): Sequential(\n",
       "        (0): Conv2dNormActivation(\n",
       "          (0): ConvBn2d(\n",
       "            112, 672, kernel_size=(1, 1), stride=(1, 1), bias=False\n",
       "            (bn): BatchNorm2d(672, eps=0.001, momentum=0.01, affine=True, track_running_stats=True)\n",
       "            (weight_fake_quant): FusedMovingAvgObsFakeQuantize(\n",
       "              fake_quant_enabled=tensor([1]), observer_enabled=tensor([1]), scale=tensor([1.]), zero_point=tensor([0], dtype=torch.int32), dtype=torch.qint8, quant_min=-128, quant_max=127, qscheme=torch.per_channel_symmetric, reduce_range=False\n",
       "              (activation_post_process): MovingAveragePerChannelMinMaxObserver(min_val=tensor([]), max_val=tensor([]))\n",
       "            )\n",
       "            (activation_post_process): FusedMovingAvgObsFakeQuantize(\n",
       "              fake_quant_enabled=tensor([1]), observer_enabled=tensor([1]), scale=tensor([1.]), zero_point=tensor([0], dtype=torch.int32), dtype=torch.quint8, quant_min=0, quant_max=127, qscheme=torch.per_tensor_affine, reduce_range=True\n",
       "              (activation_post_process): MovingAverageMinMaxObserver(min_val=inf, max_val=-inf)\n",
       "            )\n",
       "          )\n",
       "          (1): Identity()\n",
       "          (2): Hardswish(\n",
       "            (activation_post_process): FusedMovingAvgObsFakeQuantize(\n",
       "              fake_quant_enabled=tensor([1]), observer_enabled=tensor([1]), scale=tensor([1.]), zero_point=tensor([0], dtype=torch.int32), dtype=torch.quint8, quant_min=0, quant_max=127, qscheme=torch.per_tensor_affine, reduce_range=True\n",
       "              (activation_post_process): MovingAverageMinMaxObserver(min_val=inf, max_val=-inf)\n",
       "            )\n",
       "          )\n",
       "        )\n",
       "        (1): Conv2dNormActivation(\n",
       "          (0): ConvBn2d(\n",
       "            672, 672, kernel_size=(3, 3), stride=(1, 1), padding=(1, 1), groups=672, bias=False\n",
       "            (bn): BatchNorm2d(672, eps=0.001, momentum=0.01, affine=True, track_running_stats=True)\n",
       "            (weight_fake_quant): FusedMovingAvgObsFakeQuantize(\n",
       "              fake_quant_enabled=tensor([1]), observer_enabled=tensor([1]), scale=tensor([1.]), zero_point=tensor([0], dtype=torch.int32), dtype=torch.qint8, quant_min=-128, quant_max=127, qscheme=torch.per_channel_symmetric, reduce_range=False\n",
       "              (activation_post_process): MovingAveragePerChannelMinMaxObserver(min_val=tensor([]), max_val=tensor([]))\n",
       "            )\n",
       "            (activation_post_process): FusedMovingAvgObsFakeQuantize(\n",
       "              fake_quant_enabled=tensor([1]), observer_enabled=tensor([1]), scale=tensor([1.]), zero_point=tensor([0], dtype=torch.int32), dtype=torch.quint8, quant_min=0, quant_max=127, qscheme=torch.per_tensor_affine, reduce_range=True\n",
       "              (activation_post_process): MovingAverageMinMaxObserver(min_val=inf, max_val=-inf)\n",
       "            )\n",
       "          )\n",
       "          (1): Identity()\n",
       "          (2): Hardswish(\n",
       "            (activation_post_process): FusedMovingAvgObsFakeQuantize(\n",
       "              fake_quant_enabled=tensor([1]), observer_enabled=tensor([1]), scale=tensor([1.]), zero_point=tensor([0], dtype=torch.int32), dtype=torch.quint8, quant_min=0, quant_max=127, qscheme=torch.per_tensor_affine, reduce_range=True\n",
       "              (activation_post_process): MovingAverageMinMaxObserver(min_val=inf, max_val=-inf)\n",
       "            )\n",
       "          )\n",
       "        )\n",
       "        (2): QuantizableSqueezeExcitation(\n",
       "          (avgpool): AdaptiveAvgPool2d(output_size=1)\n",
       "          (fc1): ConvReLU2d(\n",
       "            672, 168, kernel_size=(1, 1), stride=(1, 1)\n",
       "            (weight_fake_quant): FusedMovingAvgObsFakeQuantize(\n",
       "              fake_quant_enabled=tensor([1]), observer_enabled=tensor([1]), scale=tensor([1.]), zero_point=tensor([0], dtype=torch.int32), dtype=torch.qint8, quant_min=-128, quant_max=127, qscheme=torch.per_channel_symmetric, reduce_range=False\n",
       "              (activation_post_process): MovingAveragePerChannelMinMaxObserver(min_val=tensor([]), max_val=tensor([]))\n",
       "            )\n",
       "            (activation_post_process): FusedMovingAvgObsFakeQuantize(\n",
       "              fake_quant_enabled=tensor([1]), observer_enabled=tensor([1]), scale=tensor([1.]), zero_point=tensor([0], dtype=torch.int32), dtype=torch.quint8, quant_min=0, quant_max=127, qscheme=torch.per_tensor_affine, reduce_range=True\n",
       "              (activation_post_process): MovingAverageMinMaxObserver(min_val=inf, max_val=-inf)\n",
       "            )\n",
       "          )\n",
       "          (fc2): Conv2d(\n",
       "            168, 672, kernel_size=(1, 1), stride=(1, 1)\n",
       "            (weight_fake_quant): FusedMovingAvgObsFakeQuantize(\n",
       "              fake_quant_enabled=tensor([1]), observer_enabled=tensor([1]), scale=tensor([1.]), zero_point=tensor([0], dtype=torch.int32), dtype=torch.qint8, quant_min=-128, quant_max=127, qscheme=torch.per_channel_symmetric, reduce_range=False\n",
       "              (activation_post_process): MovingAveragePerChannelMinMaxObserver(min_val=tensor([]), max_val=tensor([]))\n",
       "            )\n",
       "            (activation_post_process): FusedMovingAvgObsFakeQuantize(\n",
       "              fake_quant_enabled=tensor([1]), observer_enabled=tensor([1]), scale=tensor([1.]), zero_point=tensor([0], dtype=torch.int32), dtype=torch.quint8, quant_min=0, quant_max=127, qscheme=torch.per_tensor_affine, reduce_range=True\n",
       "              (activation_post_process): MovingAverageMinMaxObserver(min_val=inf, max_val=-inf)\n",
       "            )\n",
       "          )\n",
       "          (activation): Identity()\n",
       "          (scale_activation): Hardsigmoid(\n",
       "            (activation_post_process): FixedQParamsFakeQuantize(\n",
       "              fake_quant_enabled=tensor([1], dtype=torch.uint8), observer_enabled=tensor([1], dtype=torch.uint8), scale=tensor([0.0039]), zero_point=tensor([0], dtype=torch.int32), dtype=torch.quint8, quant_min=0, quant_max=255, qscheme=torch.per_tensor_affine\n",
       "              (activation_post_process): FixedQParamsObserver()\n",
       "            )\n",
       "          )\n",
       "          (skip_mul): FloatFunctional(\n",
       "            (activation_post_process): FusedMovingAvgObsFakeQuantize(\n",
       "              fake_quant_enabled=tensor([1]), observer_enabled=tensor([1]), scale=tensor([1.]), zero_point=tensor([0], dtype=torch.int32), dtype=torch.quint8, quant_min=0, quant_max=127, qscheme=torch.per_tensor_affine, reduce_range=True\n",
       "              (activation_post_process): MovingAverageMinMaxObserver(min_val=inf, max_val=-inf)\n",
       "            )\n",
       "          )\n",
       "        )\n",
       "        (3): Conv2dNormActivation(\n",
       "          (0): ConvBn2d(\n",
       "            672, 112, kernel_size=(1, 1), stride=(1, 1), bias=False\n",
       "            (bn): BatchNorm2d(112, eps=0.001, momentum=0.01, affine=True, track_running_stats=True)\n",
       "            (weight_fake_quant): FusedMovingAvgObsFakeQuantize(\n",
       "              fake_quant_enabled=tensor([1]), observer_enabled=tensor([1]), scale=tensor([1.]), zero_point=tensor([0], dtype=torch.int32), dtype=torch.qint8, quant_min=-128, quant_max=127, qscheme=torch.per_channel_symmetric, reduce_range=False\n",
       "              (activation_post_process): MovingAveragePerChannelMinMaxObserver(min_val=tensor([]), max_val=tensor([]))\n",
       "            )\n",
       "            (activation_post_process): FusedMovingAvgObsFakeQuantize(\n",
       "              fake_quant_enabled=tensor([1]), observer_enabled=tensor([1]), scale=tensor([1.]), zero_point=tensor([0], dtype=torch.int32), dtype=torch.quint8, quant_min=0, quant_max=127, qscheme=torch.per_tensor_affine, reduce_range=True\n",
       "              (activation_post_process): MovingAverageMinMaxObserver(min_val=inf, max_val=-inf)\n",
       "            )\n",
       "          )\n",
       "          (1): Identity()\n",
       "        )\n",
       "      )\n",
       "      (skip_add): FloatFunctional(\n",
       "        (activation_post_process): FusedMovingAvgObsFakeQuantize(\n",
       "          fake_quant_enabled=tensor([1]), observer_enabled=tensor([1]), scale=tensor([1.]), zero_point=tensor([0], dtype=torch.int32), dtype=torch.quint8, quant_min=0, quant_max=127, qscheme=torch.per_tensor_affine, reduce_range=True\n",
       "          (activation_post_process): MovingAverageMinMaxObserver(min_val=inf, max_val=-inf)\n",
       "        )\n",
       "      )\n",
       "    )\n",
       "    (13): QuantizableInvertedResidual(\n",
       "      (block): Sequential(\n",
       "        (0): Conv2dNormActivation(\n",
       "          (0): ConvBn2d(\n",
       "            112, 672, kernel_size=(1, 1), stride=(1, 1), bias=False\n",
       "            (bn): BatchNorm2d(672, eps=0.001, momentum=0.01, affine=True, track_running_stats=True)\n",
       "            (weight_fake_quant): FusedMovingAvgObsFakeQuantize(\n",
       "              fake_quant_enabled=tensor([1]), observer_enabled=tensor([1]), scale=tensor([1.]), zero_point=tensor([0], dtype=torch.int32), dtype=torch.qint8, quant_min=-128, quant_max=127, qscheme=torch.per_channel_symmetric, reduce_range=False\n",
       "              (activation_post_process): MovingAveragePerChannelMinMaxObserver(min_val=tensor([]), max_val=tensor([]))\n",
       "            )\n",
       "            (activation_post_process): FusedMovingAvgObsFakeQuantize(\n",
       "              fake_quant_enabled=tensor([1]), observer_enabled=tensor([1]), scale=tensor([1.]), zero_point=tensor([0], dtype=torch.int32), dtype=torch.quint8, quant_min=0, quant_max=127, qscheme=torch.per_tensor_affine, reduce_range=True\n",
       "              (activation_post_process): MovingAverageMinMaxObserver(min_val=inf, max_val=-inf)\n",
       "            )\n",
       "          )\n",
       "          (1): Identity()\n",
       "          (2): Hardswish(\n",
       "            (activation_post_process): FusedMovingAvgObsFakeQuantize(\n",
       "              fake_quant_enabled=tensor([1]), observer_enabled=tensor([1]), scale=tensor([1.]), zero_point=tensor([0], dtype=torch.int32), dtype=torch.quint8, quant_min=0, quant_max=127, qscheme=torch.per_tensor_affine, reduce_range=True\n",
       "              (activation_post_process): MovingAverageMinMaxObserver(min_val=inf, max_val=-inf)\n",
       "            )\n",
       "          )\n",
       "        )\n",
       "        (1): Conv2dNormActivation(\n",
       "          (0): ConvBn2d(\n",
       "            672, 672, kernel_size=(5, 5), stride=(2, 2), padding=(2, 2), groups=672, bias=False\n",
       "            (bn): BatchNorm2d(672, eps=0.001, momentum=0.01, affine=True, track_running_stats=True)\n",
       "            (weight_fake_quant): FusedMovingAvgObsFakeQuantize(\n",
       "              fake_quant_enabled=tensor([1]), observer_enabled=tensor([1]), scale=tensor([1.]), zero_point=tensor([0], dtype=torch.int32), dtype=torch.qint8, quant_min=-128, quant_max=127, qscheme=torch.per_channel_symmetric, reduce_range=False\n",
       "              (activation_post_process): MovingAveragePerChannelMinMaxObserver(min_val=tensor([]), max_val=tensor([]))\n",
       "            )\n",
       "            (activation_post_process): FusedMovingAvgObsFakeQuantize(\n",
       "              fake_quant_enabled=tensor([1]), observer_enabled=tensor([1]), scale=tensor([1.]), zero_point=tensor([0], dtype=torch.int32), dtype=torch.quint8, quant_min=0, quant_max=127, qscheme=torch.per_tensor_affine, reduce_range=True\n",
       "              (activation_post_process): MovingAverageMinMaxObserver(min_val=inf, max_val=-inf)\n",
       "            )\n",
       "          )\n",
       "          (1): Identity()\n",
       "          (2): Hardswish(\n",
       "            (activation_post_process): FusedMovingAvgObsFakeQuantize(\n",
       "              fake_quant_enabled=tensor([1]), observer_enabled=tensor([1]), scale=tensor([1.]), zero_point=tensor([0], dtype=torch.int32), dtype=torch.quint8, quant_min=0, quant_max=127, qscheme=torch.per_tensor_affine, reduce_range=True\n",
       "              (activation_post_process): MovingAverageMinMaxObserver(min_val=inf, max_val=-inf)\n",
       "            )\n",
       "          )\n",
       "        )\n",
       "        (2): QuantizableSqueezeExcitation(\n",
       "          (avgpool): AdaptiveAvgPool2d(output_size=1)\n",
       "          (fc1): ConvReLU2d(\n",
       "            672, 168, kernel_size=(1, 1), stride=(1, 1)\n",
       "            (weight_fake_quant): FusedMovingAvgObsFakeQuantize(\n",
       "              fake_quant_enabled=tensor([1]), observer_enabled=tensor([1]), scale=tensor([1.]), zero_point=tensor([0], dtype=torch.int32), dtype=torch.qint8, quant_min=-128, quant_max=127, qscheme=torch.per_channel_symmetric, reduce_range=False\n",
       "              (activation_post_process): MovingAveragePerChannelMinMaxObserver(min_val=tensor([]), max_val=tensor([]))\n",
       "            )\n",
       "            (activation_post_process): FusedMovingAvgObsFakeQuantize(\n",
       "              fake_quant_enabled=tensor([1]), observer_enabled=tensor([1]), scale=tensor([1.]), zero_point=tensor([0], dtype=torch.int32), dtype=torch.quint8, quant_min=0, quant_max=127, qscheme=torch.per_tensor_affine, reduce_range=True\n",
       "              (activation_post_process): MovingAverageMinMaxObserver(min_val=inf, max_val=-inf)\n",
       "            )\n",
       "          )\n",
       "          (fc2): Conv2d(\n",
       "            168, 672, kernel_size=(1, 1), stride=(1, 1)\n",
       "            (weight_fake_quant): FusedMovingAvgObsFakeQuantize(\n",
       "              fake_quant_enabled=tensor([1]), observer_enabled=tensor([1]), scale=tensor([1.]), zero_point=tensor([0], dtype=torch.int32), dtype=torch.qint8, quant_min=-128, quant_max=127, qscheme=torch.per_channel_symmetric, reduce_range=False\n",
       "              (activation_post_process): MovingAveragePerChannelMinMaxObserver(min_val=tensor([]), max_val=tensor([]))\n",
       "            )\n",
       "            (activation_post_process): FusedMovingAvgObsFakeQuantize(\n",
       "              fake_quant_enabled=tensor([1]), observer_enabled=tensor([1]), scale=tensor([1.]), zero_point=tensor([0], dtype=torch.int32), dtype=torch.quint8, quant_min=0, quant_max=127, qscheme=torch.per_tensor_affine, reduce_range=True\n",
       "              (activation_post_process): MovingAverageMinMaxObserver(min_val=inf, max_val=-inf)\n",
       "            )\n",
       "          )\n",
       "          (activation): Identity()\n",
       "          (scale_activation): Hardsigmoid(\n",
       "            (activation_post_process): FixedQParamsFakeQuantize(\n",
       "              fake_quant_enabled=tensor([1], dtype=torch.uint8), observer_enabled=tensor([1], dtype=torch.uint8), scale=tensor([0.0039]), zero_point=tensor([0], dtype=torch.int32), dtype=torch.quint8, quant_min=0, quant_max=255, qscheme=torch.per_tensor_affine\n",
       "              (activation_post_process): FixedQParamsObserver()\n",
       "            )\n",
       "          )\n",
       "          (skip_mul): FloatFunctional(\n",
       "            (activation_post_process): FusedMovingAvgObsFakeQuantize(\n",
       "              fake_quant_enabled=tensor([1]), observer_enabled=tensor([1]), scale=tensor([1.]), zero_point=tensor([0], dtype=torch.int32), dtype=torch.quint8, quant_min=0, quant_max=127, qscheme=torch.per_tensor_affine, reduce_range=True\n",
       "              (activation_post_process): MovingAverageMinMaxObserver(min_val=inf, max_val=-inf)\n",
       "            )\n",
       "          )\n",
       "        )\n",
       "        (3): Conv2dNormActivation(\n",
       "          (0): ConvBn2d(\n",
       "            672, 160, kernel_size=(1, 1), stride=(1, 1), bias=False\n",
       "            (bn): BatchNorm2d(160, eps=0.001, momentum=0.01, affine=True, track_running_stats=True)\n",
       "            (weight_fake_quant): FusedMovingAvgObsFakeQuantize(\n",
       "              fake_quant_enabled=tensor([1]), observer_enabled=tensor([1]), scale=tensor([1.]), zero_point=tensor([0], dtype=torch.int32), dtype=torch.qint8, quant_min=-128, quant_max=127, qscheme=torch.per_channel_symmetric, reduce_range=False\n",
       "              (activation_post_process): MovingAveragePerChannelMinMaxObserver(min_val=tensor([]), max_val=tensor([]))\n",
       "            )\n",
       "            (activation_post_process): FusedMovingAvgObsFakeQuantize(\n",
       "              fake_quant_enabled=tensor([1]), observer_enabled=tensor([1]), scale=tensor([1.]), zero_point=tensor([0], dtype=torch.int32), dtype=torch.quint8, quant_min=0, quant_max=127, qscheme=torch.per_tensor_affine, reduce_range=True\n",
       "              (activation_post_process): MovingAverageMinMaxObserver(min_val=inf, max_val=-inf)\n",
       "            )\n",
       "          )\n",
       "          (1): Identity()\n",
       "        )\n",
       "      )\n",
       "      (skip_add): FloatFunctional(\n",
       "        (activation_post_process): FusedMovingAvgObsFakeQuantize(\n",
       "          fake_quant_enabled=tensor([1]), observer_enabled=tensor([1]), scale=tensor([1.]), zero_point=tensor([0], dtype=torch.int32), dtype=torch.quint8, quant_min=0, quant_max=127, qscheme=torch.per_tensor_affine, reduce_range=True\n",
       "          (activation_post_process): MovingAverageMinMaxObserver(min_val=inf, max_val=-inf)\n",
       "        )\n",
       "      )\n",
       "    )\n",
       "    (14): QuantizableInvertedResidual(\n",
       "      (block): Sequential(\n",
       "        (0): Conv2dNormActivation(\n",
       "          (0): ConvBn2d(\n",
       "            160, 960, kernel_size=(1, 1), stride=(1, 1), bias=False\n",
       "            (bn): BatchNorm2d(960, eps=0.001, momentum=0.01, affine=True, track_running_stats=True)\n",
       "            (weight_fake_quant): FusedMovingAvgObsFakeQuantize(\n",
       "              fake_quant_enabled=tensor([1]), observer_enabled=tensor([1]), scale=tensor([1.]), zero_point=tensor([0], dtype=torch.int32), dtype=torch.qint8, quant_min=-128, quant_max=127, qscheme=torch.per_channel_symmetric, reduce_range=False\n",
       "              (activation_post_process): MovingAveragePerChannelMinMaxObserver(min_val=tensor([]), max_val=tensor([]))\n",
       "            )\n",
       "            (activation_post_process): FusedMovingAvgObsFakeQuantize(\n",
       "              fake_quant_enabled=tensor([1]), observer_enabled=tensor([1]), scale=tensor([1.]), zero_point=tensor([0], dtype=torch.int32), dtype=torch.quint8, quant_min=0, quant_max=127, qscheme=torch.per_tensor_affine, reduce_range=True\n",
       "              (activation_post_process): MovingAverageMinMaxObserver(min_val=inf, max_val=-inf)\n",
       "            )\n",
       "          )\n",
       "          (1): Identity()\n",
       "          (2): Hardswish(\n",
       "            (activation_post_process): FusedMovingAvgObsFakeQuantize(\n",
       "              fake_quant_enabled=tensor([1]), observer_enabled=tensor([1]), scale=tensor([1.]), zero_point=tensor([0], dtype=torch.int32), dtype=torch.quint8, quant_min=0, quant_max=127, qscheme=torch.per_tensor_affine, reduce_range=True\n",
       "              (activation_post_process): MovingAverageMinMaxObserver(min_val=inf, max_val=-inf)\n",
       "            )\n",
       "          )\n",
       "        )\n",
       "        (1): Conv2dNormActivation(\n",
       "          (0): ConvBn2d(\n",
       "            960, 960, kernel_size=(5, 5), stride=(1, 1), padding=(2, 2), groups=960, bias=False\n",
       "            (bn): BatchNorm2d(960, eps=0.001, momentum=0.01, affine=True, track_running_stats=True)\n",
       "            (weight_fake_quant): FusedMovingAvgObsFakeQuantize(\n",
       "              fake_quant_enabled=tensor([1]), observer_enabled=tensor([1]), scale=tensor([1.]), zero_point=tensor([0], dtype=torch.int32), dtype=torch.qint8, quant_min=-128, quant_max=127, qscheme=torch.per_channel_symmetric, reduce_range=False\n",
       "              (activation_post_process): MovingAveragePerChannelMinMaxObserver(min_val=tensor([]), max_val=tensor([]))\n",
       "            )\n",
       "            (activation_post_process): FusedMovingAvgObsFakeQuantize(\n",
       "              fake_quant_enabled=tensor([1]), observer_enabled=tensor([1]), scale=tensor([1.]), zero_point=tensor([0], dtype=torch.int32), dtype=torch.quint8, quant_min=0, quant_max=127, qscheme=torch.per_tensor_affine, reduce_range=True\n",
       "              (activation_post_process): MovingAverageMinMaxObserver(min_val=inf, max_val=-inf)\n",
       "            )\n",
       "          )\n",
       "          (1): Identity()\n",
       "          (2): Hardswish(\n",
       "            (activation_post_process): FusedMovingAvgObsFakeQuantize(\n",
       "              fake_quant_enabled=tensor([1]), observer_enabled=tensor([1]), scale=tensor([1.]), zero_point=tensor([0], dtype=torch.int32), dtype=torch.quint8, quant_min=0, quant_max=127, qscheme=torch.per_tensor_affine, reduce_range=True\n",
       "              (activation_post_process): MovingAverageMinMaxObserver(min_val=inf, max_val=-inf)\n",
       "            )\n",
       "          )\n",
       "        )\n",
       "        (2): QuantizableSqueezeExcitation(\n",
       "          (avgpool): AdaptiveAvgPool2d(output_size=1)\n",
       "          (fc1): ConvReLU2d(\n",
       "            960, 240, kernel_size=(1, 1), stride=(1, 1)\n",
       "            (weight_fake_quant): FusedMovingAvgObsFakeQuantize(\n",
       "              fake_quant_enabled=tensor([1]), observer_enabled=tensor([1]), scale=tensor([1.]), zero_point=tensor([0], dtype=torch.int32), dtype=torch.qint8, quant_min=-128, quant_max=127, qscheme=torch.per_channel_symmetric, reduce_range=False\n",
       "              (activation_post_process): MovingAveragePerChannelMinMaxObserver(min_val=tensor([]), max_val=tensor([]))\n",
       "            )\n",
       "            (activation_post_process): FusedMovingAvgObsFakeQuantize(\n",
       "              fake_quant_enabled=tensor([1]), observer_enabled=tensor([1]), scale=tensor([1.]), zero_point=tensor([0], dtype=torch.int32), dtype=torch.quint8, quant_min=0, quant_max=127, qscheme=torch.per_tensor_affine, reduce_range=True\n",
       "              (activation_post_process): MovingAverageMinMaxObserver(min_val=inf, max_val=-inf)\n",
       "            )\n",
       "          )\n",
       "          (fc2): Conv2d(\n",
       "            240, 960, kernel_size=(1, 1), stride=(1, 1)\n",
       "            (weight_fake_quant): FusedMovingAvgObsFakeQuantize(\n",
       "              fake_quant_enabled=tensor([1]), observer_enabled=tensor([1]), scale=tensor([1.]), zero_point=tensor([0], dtype=torch.int32), dtype=torch.qint8, quant_min=-128, quant_max=127, qscheme=torch.per_channel_symmetric, reduce_range=False\n",
       "              (activation_post_process): MovingAveragePerChannelMinMaxObserver(min_val=tensor([]), max_val=tensor([]))\n",
       "            )\n",
       "            (activation_post_process): FusedMovingAvgObsFakeQuantize(\n",
       "              fake_quant_enabled=tensor([1]), observer_enabled=tensor([1]), scale=tensor([1.]), zero_point=tensor([0], dtype=torch.int32), dtype=torch.quint8, quant_min=0, quant_max=127, qscheme=torch.per_tensor_affine, reduce_range=True\n",
       "              (activation_post_process): MovingAverageMinMaxObserver(min_val=inf, max_val=-inf)\n",
       "            )\n",
       "          )\n",
       "          (activation): Identity()\n",
       "          (scale_activation): Hardsigmoid(\n",
       "            (activation_post_process): FixedQParamsFakeQuantize(\n",
       "              fake_quant_enabled=tensor([1], dtype=torch.uint8), observer_enabled=tensor([1], dtype=torch.uint8), scale=tensor([0.0039]), zero_point=tensor([0], dtype=torch.int32), dtype=torch.quint8, quant_min=0, quant_max=255, qscheme=torch.per_tensor_affine\n",
       "              (activation_post_process): FixedQParamsObserver()\n",
       "            )\n",
       "          )\n",
       "          (skip_mul): FloatFunctional(\n",
       "            (activation_post_process): FusedMovingAvgObsFakeQuantize(\n",
       "              fake_quant_enabled=tensor([1]), observer_enabled=tensor([1]), scale=tensor([1.]), zero_point=tensor([0], dtype=torch.int32), dtype=torch.quint8, quant_min=0, quant_max=127, qscheme=torch.per_tensor_affine, reduce_range=True\n",
       "              (activation_post_process): MovingAverageMinMaxObserver(min_val=inf, max_val=-inf)\n",
       "            )\n",
       "          )\n",
       "        )\n",
       "        (3): Conv2dNormActivation(\n",
       "          (0): ConvBn2d(\n",
       "            960, 160, kernel_size=(1, 1), stride=(1, 1), bias=False\n",
       "            (bn): BatchNorm2d(160, eps=0.001, momentum=0.01, affine=True, track_running_stats=True)\n",
       "            (weight_fake_quant): FusedMovingAvgObsFakeQuantize(\n",
       "              fake_quant_enabled=tensor([1]), observer_enabled=tensor([1]), scale=tensor([1.]), zero_point=tensor([0], dtype=torch.int32), dtype=torch.qint8, quant_min=-128, quant_max=127, qscheme=torch.per_channel_symmetric, reduce_range=False\n",
       "              (activation_post_process): MovingAveragePerChannelMinMaxObserver(min_val=tensor([]), max_val=tensor([]))\n",
       "            )\n",
       "            (activation_post_process): FusedMovingAvgObsFakeQuantize(\n",
       "              fake_quant_enabled=tensor([1]), observer_enabled=tensor([1]), scale=tensor([1.]), zero_point=tensor([0], dtype=torch.int32), dtype=torch.quint8, quant_min=0, quant_max=127, qscheme=torch.per_tensor_affine, reduce_range=True\n",
       "              (activation_post_process): MovingAverageMinMaxObserver(min_val=inf, max_val=-inf)\n",
       "            )\n",
       "          )\n",
       "          (1): Identity()\n",
       "        )\n",
       "      )\n",
       "      (skip_add): FloatFunctional(\n",
       "        (activation_post_process): FusedMovingAvgObsFakeQuantize(\n",
       "          fake_quant_enabled=tensor([1]), observer_enabled=tensor([1]), scale=tensor([1.]), zero_point=tensor([0], dtype=torch.int32), dtype=torch.quint8, quant_min=0, quant_max=127, qscheme=torch.per_tensor_affine, reduce_range=True\n",
       "          (activation_post_process): MovingAverageMinMaxObserver(min_val=inf, max_val=-inf)\n",
       "        )\n",
       "      )\n",
       "    )\n",
       "    (15): QuantizableInvertedResidual(\n",
       "      (block): Sequential(\n",
       "        (0): Conv2dNormActivation(\n",
       "          (0): ConvBn2d(\n",
       "            160, 960, kernel_size=(1, 1), stride=(1, 1), bias=False\n",
       "            (bn): BatchNorm2d(960, eps=0.001, momentum=0.01, affine=True, track_running_stats=True)\n",
       "            (weight_fake_quant): FusedMovingAvgObsFakeQuantize(\n",
       "              fake_quant_enabled=tensor([1]), observer_enabled=tensor([1]), scale=tensor([1.]), zero_point=tensor([0], dtype=torch.int32), dtype=torch.qint8, quant_min=-128, quant_max=127, qscheme=torch.per_channel_symmetric, reduce_range=False\n",
       "              (activation_post_process): MovingAveragePerChannelMinMaxObserver(min_val=tensor([]), max_val=tensor([]))\n",
       "            )\n",
       "            (activation_post_process): FusedMovingAvgObsFakeQuantize(\n",
       "              fake_quant_enabled=tensor([1]), observer_enabled=tensor([1]), scale=tensor([1.]), zero_point=tensor([0], dtype=torch.int32), dtype=torch.quint8, quant_min=0, quant_max=127, qscheme=torch.per_tensor_affine, reduce_range=True\n",
       "              (activation_post_process): MovingAverageMinMaxObserver(min_val=inf, max_val=-inf)\n",
       "            )\n",
       "          )\n",
       "          (1): Identity()\n",
       "          (2): Hardswish(\n",
       "            (activation_post_process): FusedMovingAvgObsFakeQuantize(\n",
       "              fake_quant_enabled=tensor([1]), observer_enabled=tensor([1]), scale=tensor([1.]), zero_point=tensor([0], dtype=torch.int32), dtype=torch.quint8, quant_min=0, quant_max=127, qscheme=torch.per_tensor_affine, reduce_range=True\n",
       "              (activation_post_process): MovingAverageMinMaxObserver(min_val=inf, max_val=-inf)\n",
       "            )\n",
       "          )\n",
       "        )\n",
       "        (1): Conv2dNormActivation(\n",
       "          (0): ConvBn2d(\n",
       "            960, 960, kernel_size=(5, 5), stride=(1, 1), padding=(2, 2), groups=960, bias=False\n",
       "            (bn): BatchNorm2d(960, eps=0.001, momentum=0.01, affine=True, track_running_stats=True)\n",
       "            (weight_fake_quant): FusedMovingAvgObsFakeQuantize(\n",
       "              fake_quant_enabled=tensor([1]), observer_enabled=tensor([1]), scale=tensor([1.]), zero_point=tensor([0], dtype=torch.int32), dtype=torch.qint8, quant_min=-128, quant_max=127, qscheme=torch.per_channel_symmetric, reduce_range=False\n",
       "              (activation_post_process): MovingAveragePerChannelMinMaxObserver(min_val=tensor([]), max_val=tensor([]))\n",
       "            )\n",
       "            (activation_post_process): FusedMovingAvgObsFakeQuantize(\n",
       "              fake_quant_enabled=tensor([1]), observer_enabled=tensor([1]), scale=tensor([1.]), zero_point=tensor([0], dtype=torch.int32), dtype=torch.quint8, quant_min=0, quant_max=127, qscheme=torch.per_tensor_affine, reduce_range=True\n",
       "              (activation_post_process): MovingAverageMinMaxObserver(min_val=inf, max_val=-inf)\n",
       "            )\n",
       "          )\n",
       "          (1): Identity()\n",
       "          (2): Hardswish(\n",
       "            (activation_post_process): FusedMovingAvgObsFakeQuantize(\n",
       "              fake_quant_enabled=tensor([1]), observer_enabled=tensor([1]), scale=tensor([1.]), zero_point=tensor([0], dtype=torch.int32), dtype=torch.quint8, quant_min=0, quant_max=127, qscheme=torch.per_tensor_affine, reduce_range=True\n",
       "              (activation_post_process): MovingAverageMinMaxObserver(min_val=inf, max_val=-inf)\n",
       "            )\n",
       "          )\n",
       "        )\n",
       "        (2): QuantizableSqueezeExcitation(\n",
       "          (avgpool): AdaptiveAvgPool2d(output_size=1)\n",
       "          (fc1): ConvReLU2d(\n",
       "            960, 240, kernel_size=(1, 1), stride=(1, 1)\n",
       "            (weight_fake_quant): FusedMovingAvgObsFakeQuantize(\n",
       "              fake_quant_enabled=tensor([1]), observer_enabled=tensor([1]), scale=tensor([1.]), zero_point=tensor([0], dtype=torch.int32), dtype=torch.qint8, quant_min=-128, quant_max=127, qscheme=torch.per_channel_symmetric, reduce_range=False\n",
       "              (activation_post_process): MovingAveragePerChannelMinMaxObserver(min_val=tensor([]), max_val=tensor([]))\n",
       "            )\n",
       "            (activation_post_process): FusedMovingAvgObsFakeQuantize(\n",
       "              fake_quant_enabled=tensor([1]), observer_enabled=tensor([1]), scale=tensor([1.]), zero_point=tensor([0], dtype=torch.int32), dtype=torch.quint8, quant_min=0, quant_max=127, qscheme=torch.per_tensor_affine, reduce_range=True\n",
       "              (activation_post_process): MovingAverageMinMaxObserver(min_val=inf, max_val=-inf)\n",
       "            )\n",
       "          )\n",
       "          (fc2): Conv2d(\n",
       "            240, 960, kernel_size=(1, 1), stride=(1, 1)\n",
       "            (weight_fake_quant): FusedMovingAvgObsFakeQuantize(\n",
       "              fake_quant_enabled=tensor([1]), observer_enabled=tensor([1]), scale=tensor([1.]), zero_point=tensor([0], dtype=torch.int32), dtype=torch.qint8, quant_min=-128, quant_max=127, qscheme=torch.per_channel_symmetric, reduce_range=False\n",
       "              (activation_post_process): MovingAveragePerChannelMinMaxObserver(min_val=tensor([]), max_val=tensor([]))\n",
       "            )\n",
       "            (activation_post_process): FusedMovingAvgObsFakeQuantize(\n",
       "              fake_quant_enabled=tensor([1]), observer_enabled=tensor([1]), scale=tensor([1.]), zero_point=tensor([0], dtype=torch.int32), dtype=torch.quint8, quant_min=0, quant_max=127, qscheme=torch.per_tensor_affine, reduce_range=True\n",
       "              (activation_post_process): MovingAverageMinMaxObserver(min_val=inf, max_val=-inf)\n",
       "            )\n",
       "          )\n",
       "          (activation): Identity()\n",
       "          (scale_activation): Hardsigmoid(\n",
       "            (activation_post_process): FixedQParamsFakeQuantize(\n",
       "              fake_quant_enabled=tensor([1], dtype=torch.uint8), observer_enabled=tensor([1], dtype=torch.uint8), scale=tensor([0.0039]), zero_point=tensor([0], dtype=torch.int32), dtype=torch.quint8, quant_min=0, quant_max=255, qscheme=torch.per_tensor_affine\n",
       "              (activation_post_process): FixedQParamsObserver()\n",
       "            )\n",
       "          )\n",
       "          (skip_mul): FloatFunctional(\n",
       "            (activation_post_process): FusedMovingAvgObsFakeQuantize(\n",
       "              fake_quant_enabled=tensor([1]), observer_enabled=tensor([1]), scale=tensor([1.]), zero_point=tensor([0], dtype=torch.int32), dtype=torch.quint8, quant_min=0, quant_max=127, qscheme=torch.per_tensor_affine, reduce_range=True\n",
       "              (activation_post_process): MovingAverageMinMaxObserver(min_val=inf, max_val=-inf)\n",
       "            )\n",
       "          )\n",
       "        )\n",
       "        (3): Conv2dNormActivation(\n",
       "          (0): ConvBn2d(\n",
       "            960, 160, kernel_size=(1, 1), stride=(1, 1), bias=False\n",
       "            (bn): BatchNorm2d(160, eps=0.001, momentum=0.01, affine=True, track_running_stats=True)\n",
       "            (weight_fake_quant): FusedMovingAvgObsFakeQuantize(\n",
       "              fake_quant_enabled=tensor([1]), observer_enabled=tensor([1]), scale=tensor([1.]), zero_point=tensor([0], dtype=torch.int32), dtype=torch.qint8, quant_min=-128, quant_max=127, qscheme=torch.per_channel_symmetric, reduce_range=False\n",
       "              (activation_post_process): MovingAveragePerChannelMinMaxObserver(min_val=tensor([]), max_val=tensor([]))\n",
       "            )\n",
       "            (activation_post_process): FusedMovingAvgObsFakeQuantize(\n",
       "              fake_quant_enabled=tensor([1]), observer_enabled=tensor([1]), scale=tensor([1.]), zero_point=tensor([0], dtype=torch.int32), dtype=torch.quint8, quant_min=0, quant_max=127, qscheme=torch.per_tensor_affine, reduce_range=True\n",
       "              (activation_post_process): MovingAverageMinMaxObserver(min_val=inf, max_val=-inf)\n",
       "            )\n",
       "          )\n",
       "          (1): Identity()\n",
       "        )\n",
       "      )\n",
       "      (skip_add): FloatFunctional(\n",
       "        (activation_post_process): FusedMovingAvgObsFakeQuantize(\n",
       "          fake_quant_enabled=tensor([1]), observer_enabled=tensor([1]), scale=tensor([1.]), zero_point=tensor([0], dtype=torch.int32), dtype=torch.quint8, quant_min=0, quant_max=127, qscheme=torch.per_tensor_affine, reduce_range=True\n",
       "          (activation_post_process): MovingAverageMinMaxObserver(min_val=inf, max_val=-inf)\n",
       "        )\n",
       "      )\n",
       "    )\n",
       "    (16): Conv2dNormActivation(\n",
       "      (0): ConvBn2d(\n",
       "        160, 960, kernel_size=(1, 1), stride=(1, 1), bias=False\n",
       "        (bn): BatchNorm2d(960, eps=0.001, momentum=0.01, affine=True, track_running_stats=True)\n",
       "        (weight_fake_quant): FusedMovingAvgObsFakeQuantize(\n",
       "          fake_quant_enabled=tensor([1]), observer_enabled=tensor([1]), scale=tensor([1.]), zero_point=tensor([0], dtype=torch.int32), dtype=torch.qint8, quant_min=-128, quant_max=127, qscheme=torch.per_channel_symmetric, reduce_range=False\n",
       "          (activation_post_process): MovingAveragePerChannelMinMaxObserver(min_val=tensor([]), max_val=tensor([]))\n",
       "        )\n",
       "        (activation_post_process): FusedMovingAvgObsFakeQuantize(\n",
       "          fake_quant_enabled=tensor([1]), observer_enabled=tensor([1]), scale=tensor([1.]), zero_point=tensor([0], dtype=torch.int32), dtype=torch.quint8, quant_min=0, quant_max=127, qscheme=torch.per_tensor_affine, reduce_range=True\n",
       "          (activation_post_process): MovingAverageMinMaxObserver(min_val=inf, max_val=-inf)\n",
       "        )\n",
       "      )\n",
       "      (1): Identity()\n",
       "      (2): Hardswish(\n",
       "        (activation_post_process): FusedMovingAvgObsFakeQuantize(\n",
       "          fake_quant_enabled=tensor([1]), observer_enabled=tensor([1]), scale=tensor([1.]), zero_point=tensor([0], dtype=torch.int32), dtype=torch.quint8, quant_min=0, quant_max=127, qscheme=torch.per_tensor_affine, reduce_range=True\n",
       "          (activation_post_process): MovingAverageMinMaxObserver(min_val=inf, max_val=-inf)\n",
       "        )\n",
       "      )\n",
       "    )\n",
       "  )\n",
       "  (avgpool): AdaptiveAvgPool2d(output_size=1)\n",
       "  (classifier): Sequential(\n",
       "    (0): Linear(\n",
       "      in_features=960, out_features=1280, bias=True\n",
       "      (weight_fake_quant): FusedMovingAvgObsFakeQuantize(\n",
       "        fake_quant_enabled=tensor([1]), observer_enabled=tensor([1]), scale=tensor([1.]), zero_point=tensor([0], dtype=torch.int32), dtype=torch.qint8, quant_min=-128, quant_max=127, qscheme=torch.per_channel_symmetric, reduce_range=False\n",
       "        (activation_post_process): MovingAveragePerChannelMinMaxObserver(min_val=tensor([]), max_val=tensor([]))\n",
       "      )\n",
       "      (activation_post_process): FusedMovingAvgObsFakeQuantize(\n",
       "        fake_quant_enabled=tensor([1]), observer_enabled=tensor([1]), scale=tensor([1.]), zero_point=tensor([0], dtype=torch.int32), dtype=torch.quint8, quant_min=0, quant_max=127, qscheme=torch.per_tensor_affine, reduce_range=True\n",
       "        (activation_post_process): MovingAverageMinMaxObserver(min_val=inf, max_val=-inf)\n",
       "      )\n",
       "    )\n",
       "    (1): Hardswish(\n",
       "      (activation_post_process): FusedMovingAvgObsFakeQuantize(\n",
       "        fake_quant_enabled=tensor([1]), observer_enabled=tensor([1]), scale=tensor([1.]), zero_point=tensor([0], dtype=torch.int32), dtype=torch.quint8, quant_min=0, quant_max=127, qscheme=torch.per_tensor_affine, reduce_range=True\n",
       "        (activation_post_process): MovingAverageMinMaxObserver(min_val=inf, max_val=-inf)\n",
       "      )\n",
       "    )\n",
       "    (2): Dropout(p=0.2, inplace=True)\n",
       "    (3): Linear(\n",
       "      in_features=1280, out_features=90, bias=True\n",
       "      (weight_fake_quant): FusedMovingAvgObsFakeQuantize(\n",
       "        fake_quant_enabled=tensor([1]), observer_enabled=tensor([1]), scale=tensor([1.]), zero_point=tensor([0], dtype=torch.int32), dtype=torch.qint8, quant_min=-128, quant_max=127, qscheme=torch.per_channel_symmetric, reduce_range=False\n",
       "        (activation_post_process): MovingAveragePerChannelMinMaxObserver(min_val=tensor([]), max_val=tensor([]))\n",
       "      )\n",
       "      (activation_post_process): FusedMovingAvgObsFakeQuantize(\n",
       "        fake_quant_enabled=tensor([1]), observer_enabled=tensor([1]), scale=tensor([1.]), zero_point=tensor([0], dtype=torch.int32), dtype=torch.quint8, quant_min=0, quant_max=127, qscheme=torch.per_tensor_affine, reduce_range=True\n",
       "        (activation_post_process): MovingAverageMinMaxObserver(min_val=inf, max_val=-inf)\n",
       "      )\n",
       "    )\n",
       "  )\n",
       "  (quant): QuantStub(\n",
       "    (activation_post_process): FusedMovingAvgObsFakeQuantize(\n",
       "      fake_quant_enabled=tensor([1]), observer_enabled=tensor([1]), scale=tensor([1.]), zero_point=tensor([0], dtype=torch.int32), dtype=torch.quint8, quant_min=0, quant_max=127, qscheme=torch.per_tensor_affine, reduce_range=True\n",
       "      (activation_post_process): MovingAverageMinMaxObserver(min_val=inf, max_val=-inf)\n",
       "    )\n",
       "  )\n",
       "  (dequant): DeQuantStub()\n",
       ")"
      ]
     },
     "execution_count": 7,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "model.qconfig = get_default_qat_qconfig(\"fbgemm\")\n",
    "prepare_qat(model, inplace=True)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 8,
   "id": "62637141",
   "metadata": {},
   "outputs": [],
   "source": [
    "model = model.cuda(3)\n",
    "optimizer = torch.optim.Adam(model.parameters(), lr=lr)\n",
    "criterion = nn.CrossEntropyLoss()\n",
    "best_model_wts = None\n",
    "# writer = SummaryWriter()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 9,
   "id": "75e7a13a-3f06-4de6-9e6a-cea34fa8c28f",
   "metadata": {},
   "outputs": [],
   "source": [
    "def train(epoch, model):\n",
    "    model.train()\n",
    "    train_loss = 0\n",
    "    for data, label in train_loader:\n",
    "        data, label = data.cuda(3), label.cuda(3)\n",
    "        # clear the grad\n",
    "        optimizer.zero_grad()\n",
    "        output = model(data)\n",
    "        # loss function\n",
    "        loss = criterion(output, label)\n",
    "        loss.backward()\n",
    "        optimizer.step()\n",
    "        # scheduler.step()\n",
    "        train_loss += loss.item() * data.size(0)\n",
    "    train_loss = train_loss / len(train_loader.dataset)\n",
    "    # Re-quantize the model\n",
    "    # model = quantize_dynamic(model, {'': torch.quantization.default_dynamic_qconfig}, dtype=torch.qint8)\n",
    "    # writer.add_scalar(\"Loss/train\", train_loss, epoch)\n",
    "    # loss_vec32.append(train_loss)\n",
    "    print('Epoch: {} \\tTraining Loss: {:.6f}'.format(epoch, train_loss))"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 10,
   "id": "24a8cfb1-cfaa-42ac-9fdd-fd1d708ac2d6",
   "metadata": {},
   "outputs": [],
   "source": [
    "def test(epoch, model):\n",
    "    quantized_model = torch.quantization.convert(model.cpu().eval(), inplace=False)\n",
    "    quantized_model.eval()\n",
    "    idx = 0\n",
    "    ans = 0.0\n",
    "    best_acc = 0.0\n",
    "    with torch.no_grad():\n",
    "        for data, label in test_loader:\n",
    "            output = quantized_model(data)\n",
    "            preds = torch.argmax(output, 1)\n",
    "            unique_values, counts = torch.unique(preds, return_counts=True)\n",
    "            pres = unique_values[counts.argmax()]\n",
    "            if pres.item() == idx:\n",
    "                ans += 1\n",
    "            idx += 1\n",
    "    acc = ans / 90\n",
    "    if acc > best_acc:\n",
    "        best_model_wts = copy.deepcopy(quantized_model.state_dict())\n",
    "        # print(best_model_wts)\n",
    "    print('Epoch: {} Accuracy: {:6f}'.format(epoch, acc))\n",
    "    return best_model_wts"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 11,
   "id": "ea3e2e2a-0723-4ceb-97ae-ec1f28316dcf",
   "metadata": {
    "scrolled": true
   },
   "outputs": [
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "/data/lambd4/anaconda3/py39/lib/python3.9/site-packages/torch/ao/quantization/fake_quantize.py:343: UserWarning: _aminmax is deprecated as of PyTorch 1.11 and will be removed in a future release. Use aminmax instead. This warning will only appear once per process. (Triggered internally at /opt/conda/conda-bld/pytorch_1702400441250/work/aten/src/ATen/native/ReduceAllOps.cpp:72.)\n",
      "  return torch.fused_moving_avg_obs_fake_quant(\n",
      "/data/lambd4/anaconda3/py39/lib/python3.9/site-packages/torch/ao/quantization/fake_quantize.py:343: UserWarning: _aminmax is deprecated as of PyTorch 1.11 and will be removed in a future release. Use aminmax instead. This warning will only appear once per process. (Triggered internally at /opt/conda/conda-bld/pytorch_1702400441250/work/aten/src/ATen/native/TensorCompare.cpp:677.)\n",
      "  return torch.fused_moving_avg_obs_fake_quant(\n"
     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Epoch: 1 \tTraining Loss: 4.314790\n",
      "Epoch: 2 \tTraining Loss: 3.867196\n",
      "Epoch: 3 \tTraining Loss: 3.877579\n",
      "Epoch: 4 \tTraining Loss: 3.649849\n",
      "Epoch: 5 \tTraining Loss: 3.932557\n",
      "Epoch: 6 \tTraining Loss: 3.877930\n",
      "Epoch: 7 \tTraining Loss: 7.346864\n",
      "Epoch: 8 \tTraining Loss: 3.705178\n",
      "Epoch: 9 \tTraining Loss: 3.245525\n",
      "Epoch: 10 \tTraining Loss: 2.982609\n",
      "Epoch: 11 \tTraining Loss: 2.738164\n",
      "Epoch: 12 \tTraining Loss: 2.533820\n",
      "Epoch: 13 \tTraining Loss: 2.373858\n",
      "Epoch: 14 \tTraining Loss: 2.236841\n",
      "Epoch: 15 \tTraining Loss: 2.129011\n",
      "Epoch: 16 \tTraining Loss: 1.948139\n",
      "Epoch: 17 \tTraining Loss: 1.823758\n",
      "Epoch: 18 \tTraining Loss: 1.720606\n",
      "Epoch: 19 \tTraining Loss: 1.533189\n",
      "Epoch: 20 \tTraining Loss: 1.545346\n",
      "Epoch: 21 \tTraining Loss: 1.353186\n",
      "Epoch: 22 \tTraining Loss: 1.282354\n",
      "Epoch: 23 \tTraining Loss: 1.239254\n",
      "Epoch: 24 \tTraining Loss: 1.184227\n",
      "Epoch: 25 \tTraining Loss: 1.005921\n",
      "Epoch: 26 \tTraining Loss: 0.940876\n",
      "Epoch: 27 \tTraining Loss: 0.852463\n",
      "Epoch: 28 \tTraining Loss: 0.916623\n",
      "Epoch: 29 \tTraining Loss: 0.788116\n",
      "Epoch: 30 \tTraining Loss: 0.646034\n",
      "Epoch: 31 \tTraining Loss: 0.611368\n",
      "Epoch: 32 \tTraining Loss: 0.648989\n",
      "Epoch: 33 \tTraining Loss: 0.553789\n",
      "Epoch: 34 \tTraining Loss: 0.535567\n",
      "Epoch: 35 \tTraining Loss: 0.443088\n",
      "Epoch: 36 \tTraining Loss: 0.513277\n",
      "Epoch: 37 \tTraining Loss: 0.429350\n",
      "Epoch: 38 \tTraining Loss: 0.431545\n",
      "Epoch: 39 \tTraining Loss: 0.446362\n",
      "Epoch: 40 \tTraining Loss: 0.345569\n",
      "Epoch: 41 \tTraining Loss: 0.424677\n",
      "Epoch: 42 \tTraining Loss: 0.429097\n",
      "Epoch: 43 \tTraining Loss: 0.366093\n",
      "Epoch: 44 \tTraining Loss: 0.343929\n",
      "Epoch: 45 \tTraining Loss: 0.411485\n",
      "Epoch: 46 \tTraining Loss: 0.335805\n",
      "Epoch: 47 \tTraining Loss: 0.305141\n",
      "Epoch: 48 \tTraining Loss: 0.323386\n",
      "Epoch: 49 \tTraining Loss: 0.300158\n",
      "Epoch: 50 \tTraining Loss: 0.276899\n",
      "Training time 0:03:16\n"
     ]
    }
   ],
   "source": [
    "start_time = time.time()\n",
    "for epoch in range(1, epochs + 1):\n",
    "    train(epoch, model)\n",
    "total_time = time.time() - start_time\n",
    "total_time_str = str(datetime.timedelta(seconds=int(total_time)))\n",
    "print(f\"Training time {total_time_str}\")"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 19,
   "id": "3fa3d4b1",
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "OrderedDict([('features.0.0.weight', tensor([[[[-0.6229, -0.3594,  1.5094],\n",
      "          [ 0.4073, -0.0240,  2.4917],\n",
      "          [-0.8146, -2.1083, -0.7427]],\n",
      "\n",
      "         [[-2.9469, -0.1917,  0.4552],\n",
      "          [-0.1198, -0.7427,  0.0240],\n",
      "          [ 1.2458, -0.2156,  0.3354]],\n",
      "\n",
      "         [[-0.4073,  0.3833, -1.6052],\n",
      "          [-1.1500,  0.7188,  0.9823],\n",
      "          [ 0.9583,  0.7667,  0.8625]]],\n",
      "\n",
      "\n",
      "        [[[-0.7083,  0.2471,  1.9603],\n",
      "          [-0.4118,  0.0000,  0.5271],\n",
      "          [ 0.3624, -0.8895, -0.8566]],\n",
      "\n",
      "         [[ 0.4283,  0.0988,  0.3789],\n",
      "          [ 0.2306,  0.2800,  0.2800],\n",
      "          [ 1.0543,  0.6095,  0.3954]],\n",
      "\n",
      "         [[-0.1153, -0.6589,  0.2306],\n",
      "          [-0.1483, -0.2471,  2.0921],\n",
      "          [ 0.2141, -0.0494, -0.3130]]],\n",
      "\n",
      "\n",
      "        [[[-0.4073,  0.8400, -0.0255],\n",
      "          [ 1.0819, -0.4200,  0.0127],\n",
      "          [ 0.0000, -0.3564,  0.9928]],\n",
      "\n",
      "         [[ 0.4073,  0.0127, -0.1909],\n",
      "          [ 0.1146,  0.2036,  0.3309],\n",
      "          [-0.7637, -0.5855, -0.1909]],\n",
      "\n",
      "         [[-0.8146, -1.6292, -0.2546],\n",
      "          [ 0.1018,  0.2927, -0.5473],\n",
      "          [-0.4073,  0.9546, -0.8400]]],\n",
      "\n",
      "\n",
      "        [[[-0.0540, -0.4048,  0.8636],\n",
      "          [-2.9146,  1.1335,  1.1065],\n",
      "          [ 1.2954, -1.8891,  1.5113]],\n",
      "\n",
      "         [[ 2.5098,  0.3238, -1.9701],\n",
      "          [ 1.2684, -0.5937, -0.6747],\n",
      "          [ 0.5667, -1.2684,  0.5937]],\n",
      "\n",
      "         [[-0.1889,  3.4274, -1.0795],\n",
      "          [-2.8067, -0.0270, -0.9176],\n",
      "          [ 0.5667, -1.9161,  0.5128]]],\n",
      "\n",
      "\n",
      "        [[[ 2.8990, -0.5551,  1.6654],\n",
      "          [-0.4009, -0.0308,  0.2159],\n",
      "          [ 0.6785, -1.7579, -2.7139]],\n",
      "\n",
      "         [[-1.2953, -0.1542,  2.3130],\n",
      "          [-1.2336, -0.9252,  1.2645],\n",
      "          [ 3.9167, -1.3878, -1.8813]],\n",
      "\n",
      "         [[-0.7402,  0.3701,  0.1542],\n",
      "          [ 1.4495, -0.6785,  0.8635],\n",
      "          [ 0.1234,  0.8944, -0.8944]]],\n",
      "\n",
      "\n",
      "        [[[-0.1570,  0.5338, -0.1675],\n",
      "          [ 0.5652,  0.1361,  0.5547],\n",
      "          [-0.2093,  1.3187,  0.0419]],\n",
      "\n",
      "         [[ 0.3663, -0.0733,  0.2407],\n",
      "          [-0.2198, -0.2198,  0.2303],\n",
      "          [ 0.8792, -0.5024, -0.3349]],\n",
      "\n",
      "         [[ 0.1884, -0.6175, -1.2873],\n",
      "          [ 0.5861,  0.4500, -0.1779],\n",
      "          [-0.5547, -0.6280, -0.0105]]],\n",
      "\n",
      "\n",
      "        [[[-0.6914,  0.2305,  1.8294],\n",
      "          [ 0.0720,  0.4177, -0.1729],\n",
      "          [ 0.6194,  0.5330,  0.8355]],\n",
      "\n",
      "         [[-0.3601, -0.4177, -0.3457],\n",
      "          [ 0.1584,  0.0288,  0.2305],\n",
      "          [ 0.8355,  0.0144, -0.3889]],\n",
      "\n",
      "         [[-1.4693, -0.5762,  1.5125],\n",
      "          [ 1.1668,  0.3169,  0.0288],\n",
      "          [ 0.1584, -0.0288, -0.4753]]],\n",
      "\n",
      "\n",
      "        [[[ 0.6605, -0.1275,  0.2897],\n",
      "          [ 0.0927,  0.1043, -0.2433],\n",
      "          [ 0.2202, -0.3245, -0.1854]],\n",
      "\n",
      "         [[-0.2433,  0.7648,  0.8923],\n",
      "          [ 0.0348,  0.4172, -0.0579],\n",
      "          [-0.2665,  0.8459,  1.4717]],\n",
      "\n",
      "         [[ 0.3592,  0.7532, -0.2781],\n",
      "          [ 0.4403,  0.0232,  0.0695],\n",
      "          [-0.1970, -0.5330,  0.2433]]],\n",
      "\n",
      "\n",
      "        [[[ 0.0851,  0.4595, -0.6977],\n",
      "          [ 0.5956, -0.6807, -0.6467],\n",
      "          [-0.9360,  0.2893,  0.9020]],\n",
      "\n",
      "         [[ 1.2423, -1.4976, -1.1913],\n",
      "          [-0.5956,  0.1702, -0.8679],\n",
      "          [-1.1742, -0.1191, -0.2723]],\n",
      "\n",
      "         [[ 0.7488,  0.1702,  0.2042],\n",
      "          [ 0.2553, -0.6297,  0.5276],\n",
      "          [-2.1783,  0.4935,  1.0551]]],\n",
      "\n",
      "\n",
      "        [[[-0.6017,  0.0000,  0.5014],\n",
      "          [-1.1700, -0.6519, -0.2674],\n",
      "          [ 0.0669,  2.0392, -1.7383]],\n",
      "\n",
      "         [[ 0.7856, -0.0167,  0.1504],\n",
      "          [ 0.0669,  0.1504,  0.2006],\n",
      "          [-0.5182,  1.0697,  0.9026]],\n",
      "\n",
      "         [[ 0.6519, -0.5014, -0.3844],\n",
      "          [ 1.1366, -0.3176, -0.2674],\n",
      "          [ 0.1671,  0.6519,  0.0501]]],\n",
      "\n",
      "\n",
      "        [[[-0.0868,  1.3777,  0.7051],\n",
      "          [-0.1519, -0.3363, -0.3580],\n",
      "          [-0.3146,  0.2170, -0.5641]],\n",
      "\n",
      "         [[ 0.6943, -0.2061, -1.2475],\n",
      "          [ 0.1193, -0.1302, -1.1607],\n",
      "          [-0.1953,  0.4231, -0.8027]],\n",
      "\n",
      "         [[ 0.6834, -0.7268, -0.1193],\n",
      "          [-0.0325, -0.0108, -0.2170],\n",
      "          [-0.8895, -0.5207, -0.4556]]],\n",
      "\n",
      "\n",
      "        [[[ 0.2670, -0.4227, -1.0680],\n",
      "          [ 0.4672,  0.7787,  0.0000],\n",
      "          [ 1.1570,  1.2237, -2.8034]],\n",
      "\n",
      "         [[ 1.2237, -0.2225,  2.8256],\n",
      "          [ 1.0902, -0.3782, -1.4907],\n",
      "          [-0.6675,  0.5117, -0.8455]],\n",
      "\n",
      "         [[-0.0445,  0.1780,  0.8232],\n",
      "          [-2.4029, -1.6242,  0.5340],\n",
      "          [ 2.8256,  0.0445, -1.7354]]],\n",
      "\n",
      "\n",
      "        [[[ 0.7993,  0.0799, -0.0133],\n",
      "          [ 1.1590, -1.2789,  0.0000],\n",
      "          [ 0.0533,  0.7860, -0.7194]],\n",
      "\n",
      "         [[-0.4263, -0.1465, -1.3322],\n",
      "          [-0.1599, -1.0791, -1.0525],\n",
      "          [-0.9192, -0.2265,  1.6919]],\n",
      "\n",
      "         [[-0.3863,  0.7860,  0.2265],\n",
      "          [ 0.3331, -0.9725, -0.6661],\n",
      "          [ 0.6395, -0.0533,  0.0400]]],\n",
      "\n",
      "\n",
      "        [[[ 0.3267, -0.9299, -0.0126],\n",
      "          [ 0.7666,  0.2890,  0.0000],\n",
      "          [ 0.2388, -0.5529, -0.0251]],\n",
      "\n",
      "         [[ 0.2890,  0.9174,  1.0807],\n",
      "          [-0.0880,  1.5959, -0.3896],\n",
      "          [ 0.1005, -0.9299,  0.7163]],\n",
      "\n",
      "         [[ 0.8043, -0.2890,  0.0628],\n",
      "          [-0.0754, -0.1131, -0.3896],\n",
      "          [ 0.2388,  0.4147,  1.3949]]],\n",
      "\n",
      "\n",
      "        [[[ 0.1454, -0.4121, -0.0485],\n",
      "          [ 1.0180,  0.5575, -0.7029],\n",
      "          [-0.0848,  0.1576,  0.5817]],\n",
      "\n",
      "         [[-0.1576,  0.5454, -0.5454],\n",
      "          [-1.5513,  0.3999, -0.5090],\n",
      "          [-0.5939, -0.0848, -0.4242]],\n",
      "\n",
      "         [[-0.0727, -0.3393, -0.1818],\n",
      "          [ 0.2909,  0.0848, -0.0242],\n",
      "          [ 0.2787, -0.5696, -0.7514]]],\n",
      "\n",
      "\n",
      "        [[[ 2.7437, -1.6419, -0.7994],\n",
      "          [ 0.0864, -0.6265, -0.0864],\n",
      "          [ 1.5771,  0.2593,  0.3241]],\n",
      "\n",
      "         [[ 0.0000, -0.7994,  0.2376],\n",
      "          [-0.0864, -0.1080, -0.9938],\n",
      "          [-1.3611,  0.1728,  0.5617]],\n",
      "\n",
      "         [[ 0.6697, -0.5401,  1.2314],\n",
      "          [ 0.7778, -0.3889,  1.0802],\n",
      "          [ 0.5617,  1.6203, -0.4105]]]], size=(16, 3, 3, 3),\n",
      "       dtype=torch.qint8, quantization_scheme=torch.per_channel_affine,\n",
      "       scale=tensor([0.0240, 0.0165, 0.0127, 0.0270, 0.0308, 0.0105, 0.0144, 0.0116, 0.0170,\n",
      "        0.0167, 0.0108, 0.0222, 0.0133, 0.0126, 0.0121, 0.0216],\n",
      "       dtype=torch.float64),\n",
      "       zero_point=tensor([0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0]),\n",
      "       axis=0)), ('features.0.0.bias', Parameter containing:\n",
      "tensor([ 0.9475, -3.9515,  1.8194,  0.9289, -1.5990, -0.3190, -2.2920, -4.0241,\n",
      "         3.5159, -2.0994,  3.4182, -0.6031,  2.6213, -4.1669,  2.6321, -2.1928])), ('features.0.0.scale', tensor(0.1099)), ('features.0.0.zero_point', tensor(78)), ('features.0.2.scale', tensor(0.0445)), ('features.0.2.zero_point', tensor(8)), ('features.1.block.0.0.weight', tensor([[[[-0.1408,  0.2972, -0.3128],\n",
      "          [-0.3206,  0.9854, -0.4301],\n",
      "          [ 0.5709,  0.4927, -0.0313]]],\n",
      "\n",
      "\n",
      "        [[[-0.9105, -0.7607, -0.2997],\n",
      "          [ 1.1295, -1.4752,  0.2075],\n",
      "          [-1.2793, -0.7952,  0.2881]]],\n",
      "\n",
      "\n",
      "        [[[ 0.4383, -0.6255, -0.1379],\n",
      "          [-0.2167, -0.2807, -0.0493],\n",
      "          [-0.1478, -0.1428, -0.2610]]],\n",
      "\n",
      "\n",
      "        [[[-0.3176,  0.0000,  0.1248],\n",
      "          [-0.0227, -1.4406,  0.0227],\n",
      "          [-0.5785,  0.0794,  0.1588]]],\n",
      "\n",
      "\n",
      "        [[[-2.7641,  0.6142, -0.1097],\n",
      "          [ 0.0000, -0.5265,  0.3291],\n",
      "          [ 0.9872, -0.5265, -0.8556]]],\n",
      "\n",
      "\n",
      "        [[[-0.8571, -1.1894, -0.3498],\n",
      "          [-0.1749, -0.0350,  2.2214],\n",
      "          [-0.2799,  0.2624, -0.0175]]],\n",
      "\n",
      "\n",
      "        [[[-0.2546,  0.3035,  0.3035],\n",
      "          [-0.4210, -0.2056, -0.3721],\n",
      "          [ 0.0294, -1.2338, -0.2644]]],\n",
      "\n",
      "\n",
      "        [[[ 0.0630, -0.5669,  0.2940],\n",
      "          [ 1.5328, -1.0079,  0.8399],\n",
      "          [ 2.6456,  0.3989, -1.2808]]],\n",
      "\n",
      "\n",
      "        [[[ 0.4283,  0.5438, -0.2118],\n",
      "          [ 0.2743,  0.4909, -0.5775],\n",
      "          [ 0.0818, -0.2743,  0.6112]]],\n",
      "\n",
      "\n",
      "        [[[ 0.4586, -0.5460,  1.2230],\n",
      "          [ 0.4368, -0.8954,  2.2712],\n",
      "          [-2.7954, -1.0919,  0.4586]]],\n",
      "\n",
      "\n",
      "        [[[ 0.2588, -0.2588, -0.3328],\n",
      "          [ 0.0986,  0.7149,  1.5653],\n",
      "          [ 0.4191, -0.0493, -1.3188]]],\n",
      "\n",
      "\n",
      "        [[[-0.4637,  2.0152,  0.6955],\n",
      "          [-0.6777, -0.4994, -1.5337],\n",
      "          [ 1.1057,  0.7490, -2.2828]]],\n",
      "\n",
      "\n",
      "        [[[-0.3580, -0.1114,  0.5171],\n",
      "          [ 0.9944, -0.2228, -0.1591],\n",
      "          [-0.4375,  0.3341,  0.8353]]],\n",
      "\n",
      "\n",
      "        [[[-0.2981, -0.3776,  0.9738],\n",
      "          [ 0.7353,  2.5240, -0.6161],\n",
      "          [ 1.1328, -0.0994, -0.3975]]],\n",
      "\n",
      "\n",
      "        [[[-0.4470, -0.2269, -0.0200],\n",
      "          [-0.1935,  0.2669, -0.1401],\n",
      "          [-0.1735, -0.8541,  0.4337]]],\n",
      "\n",
      "\n",
      "        [[[ 0.0374, -0.2183, -0.7982],\n",
      "          [-0.2681, -0.2245, -0.1559],\n",
      "          [-0.0686,  0.0249, -0.0062]]]], size=(16, 1, 3, 3),\n",
      "       dtype=torch.qint8, quantization_scheme=torch.per_channel_affine,\n",
      "       scale=tensor([0.0078, 0.0115, 0.0049, 0.0113, 0.0219, 0.0175, 0.0098, 0.0210, 0.0048,\n",
      "        0.0218, 0.0123, 0.0178, 0.0080, 0.0199, 0.0067, 0.0062],\n",
      "       dtype=torch.float64),\n",
      "       zero_point=tensor([0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0]),\n",
      "       axis=0)), ('features.1.block.0.0.bias', Parameter containing:\n",
      "tensor([-0.1370,  0.6850,  0.2813,  0.3556,  0.2838,  0.0488,  0.3144, -0.4637,\n",
      "        -0.2928,  0.1059, -0.1565,  0.0789, -0.2527, -0.5994,  0.1701,  0.2074])), ('features.1.block.0.0.scale', tensor(0.0577)), ('features.1.block.0.0.zero_point', tensor(0)), ('features.1.block.1.0.weight', tensor([[[[-0.2369]],\n",
      "\n",
      "         [[-0.2931]],\n",
      "\n",
      "         [[ 0.3928]],\n",
      "\n",
      "         [[ 0.7919]],\n",
      "\n",
      "         [[ 0.1559]],\n",
      "\n",
      "         [[-0.1060]],\n",
      "\n",
      "         [[ 0.5051]],\n",
      "\n",
      "         [[-0.2681]],\n",
      "\n",
      "         [[-0.0436]],\n",
      "\n",
      "         [[ 0.0873]],\n",
      "\n",
      "         [[-0.3617]],\n",
      "\n",
      "         [[-0.2494]],\n",
      "\n",
      "         [[-0.3804]],\n",
      "\n",
      "         [[-0.1871]],\n",
      "\n",
      "         [[ 0.3492]],\n",
      "\n",
      "         [[-0.0624]]],\n",
      "\n",
      "\n",
      "        [[[ 0.3075]],\n",
      "\n",
      "         [[-0.7072]],\n",
      "\n",
      "         [[-0.9839]],\n",
      "\n",
      "         [[ 0.4151]],\n",
      "\n",
      "         [[ 0.3690]],\n",
      "\n",
      "         [[-0.1076]],\n",
      "\n",
      "         [[-0.3843]],\n",
      "\n",
      "         [[-0.1384]],\n",
      "\n",
      "         [[-0.4305]],\n",
      "\n",
      "         [[ 0.2614]],\n",
      "\n",
      "         [[ 0.7303]],\n",
      "\n",
      "         [[ 0.4305]],\n",
      "\n",
      "         [[ 0.3229]],\n",
      "\n",
      "         [[-0.3613]],\n",
      "\n",
      "         [[ 0.0307]],\n",
      "\n",
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      "       scale=tensor([0.0062, 0.0077, 0.0039, 0.0066, 0.0105, 0.0079, 0.0068, 0.0121, 0.0051,\n",
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      "       dtype=torch.float64),\n",
      "       zero_point=tensor([0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0]),\n",
      "       axis=0)), ('features.1.block.1.0.bias', Parameter containing:\n",
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      "         ...,\n",
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      "       quantization_scheme=torch.per_channel_affine,\n",
      "       scale=tensor([0.0052, 0.0022, 0.0017, 0.0032, 0.0051, 0.0038, 0.0026, 0.0027, 0.0023,\n",
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      "       zero_point=tensor([0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0,\n",
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      "        0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0]),\n",
      "       axis=0)), ('features.2.block.0.0.bias', Parameter containing:\n",
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      "        -0.1614, -0.1748,  0.1214,  0.1349,  0.2422,  0.1313, -0.0465,  0.2058,\n",
      "        -0.1147,  0.1213, -0.1222, -0.2441, -0.1706, -0.0239,  0.0584,  0.3504,\n",
      "         0.2435,  0.0280, -0.0240, -0.0875,  0.1399,  0.1077, -0.2069,  0.1252,\n",
      "        -0.1404, -0.2338, -0.3359, -0.1488,  0.1020, -0.1505,  0.1056,  0.1202,\n",
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      "        -0.2280,  0.1569, -0.0688, -0.1973, -0.0079, -0.1103,  0.0296,  0.1438,\n",
      "        -0.0272,  0.0595,  0.1625,  0.0418, -0.0227, -0.0801,  0.1425,  0.2644])), ('features.2.block.0.0.scale', tensor(0.0734)), ('features.2.block.0.0.zero_point', tensor(0)), ('features.2.block.1.0.weight', tensor([[[[ 0.4848,  0.0000, -0.6215],\n",
      "          [-0.5097,  0.7707, -0.7583],\n",
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      "          [-0.3662, -0.2746,  0.0785],\n",
      "          [-0.1112,  0.0588, -0.6800]]],\n",
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      "          [ 0.0670,  1.5269,  0.8840],\n",
      "          [-0.2277, -0.3348,  1.6608]]],\n",
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      "          [-0.4800,  1.2700,  0.4400]]],\n",
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      "        [[[ 0.4892,  0.0288,  1.7697],\n",
      "          [-0.2590, -0.1870, -1.2805],\n",
      "          [-0.8777, -0.0288, -0.1583]]],\n",
      "\n",
      "\n",
      "        [[[ 0.0052, -0.1192, -0.1088],\n",
      "          [-0.1347, -0.0155, -0.6528],\n",
      "          [-0.2694,  0.0311, -0.2072]]],\n",
      "\n",
      "\n",
      "        [[[-0.3107,  0.0895, -0.2843],\n",
      "          [ 0.0105, -0.3001, -0.1685],\n",
      "          [-0.2422, -0.6740,  0.1264]]],\n",
      "\n",
      "\n",
      "        [[[-0.3606, -0.4014, -0.2926],\n",
      "          [ 0.1225, -0.7484, -0.0544],\n",
      "          [-0.0544, -0.8709, -0.1429]]],\n",
      "\n",
      "\n",
      "        [[[ 0.2459,  0.4050,  0.0723],\n",
      "          [ 0.5786, -1.8515, -0.1157],\n",
      "          [ 0.8390,  0.7666, -0.0434]]],\n",
      "\n",
      "\n",
      "        [[[ 2.0204, -2.4327, -0.2062],\n",
      "          [ 0.0206,  0.8040, -0.1649],\n",
      "          [ 0.8659, -0.3917, -0.7009]]],\n",
      "\n",
      "\n",
      "        [[[ 0.5108,  0.5778,  0.2261],\n",
      "          [ 0.4271, -0.0167,  0.3434],\n",
      "          [-0.2512, -1.0719, -0.0837]]],\n",
      "\n",
      "\n",
      "        [[[-0.4600,  1.4981,  0.1062],\n",
      "          [-0.3185, -0.1180, -0.4836],\n",
      "          [-0.3421, -0.1887, -0.2831]]],\n",
      "\n",
      "\n",
      "        [[[-0.1787,  0.4621,  0.1232],\n",
      "          [-0.0308,  0.1294,  0.7825],\n",
      "          [ 0.2958, -0.2033,  0.0986]]],\n",
      "\n",
      "\n",
      "        [[[ 0.1091,  0.6039,  0.1892],\n",
      "          [ 0.2546, -0.6984, -0.9094],\n",
      "          [ 0.2619, -0.7785, -0.3056]]],\n",
      "\n",
      "\n",
      "        [[[-0.0449, -0.4149, -0.7065],\n",
      "          [ 0.0224,  0.3140, -1.3905],\n",
      "          [-0.8410, -0.1570,  0.1682]]],\n",
      "\n",
      "\n",
      "        [[[ 0.0999,  0.2165,  0.1249],\n",
      "          [-0.0416,  1.0576,  0.5829],\n",
      "          [-0.7662,  0.4247,  0.9077]]],\n",
      "\n",
      "\n",
      "        [[[-1.2870,  0.2324,  2.2165],\n",
      "          [ 1.7338,  0.3575,  0.0536],\n",
      "          [ 1.4300, -0.2324, -1.2691]]],\n",
      "\n",
      "\n",
      "        [[[ 0.4393, -0.3815,  0.2659],\n",
      "          [-1.0636,  0.1272, -1.4798],\n",
      "          [-0.2543,  0.1965, -0.8208]]],\n",
      "\n",
      "\n",
      "        [[[ 0.0056,  0.1070,  0.2873],\n",
      "          [-0.4394, -0.0563, -0.3155],\n",
      "          [-0.1127, -0.0620, -0.7211]]],\n",
      "\n",
      "\n",
      "        [[[-0.0129,  1.0741,  0.6600],\n",
      "          [-1.6564, -1.0352, -0.6600],\n",
      "          [ 0.5435, -0.2200,  0.1553]]],\n",
      "\n",
      "\n",
      "        [[[ 0.0584,  0.0729, -0.0875],\n",
      "          [ 0.1848,  0.3647, -0.3598],\n",
      "          [-0.0146,  0.6176,  0.5592]]],\n",
      "\n",
      "\n",
      "        [[[ 0.1170,  0.8480,  1.2378],\n",
      "          [-0.7895,  0.3119, -0.7602],\n",
      "          [ 1.1599, -0.5361,  0.1657]]],\n",
      "\n",
      "\n",
      "        [[[ 0.3294, -0.0915,  0.1220],\n",
      "          [-0.2257,  0.2806,  0.7748],\n",
      "          [ 0.1037,  0.2074, -0.1220]]],\n",
      "\n",
      "\n",
      "        [[[ 0.3655,  0.7867,  0.5699],\n",
      "          [ 0.1735,  0.2664, -0.0558],\n",
      "          [-0.3717, -0.0186,  0.2602]]],\n",
      "\n",
      "\n",
      "        [[[-0.0615, -0.2014,  0.0056],\n",
      "          [ 0.3748,  0.4475,  0.7105],\n",
      "          [ 0.2406, -0.0336, -0.0839]]],\n",
      "\n",
      "\n",
      "        [[[ 0.1072,  0.1965,  0.3573],\n",
      "          [ 1.0450, -1.1165, -0.1608],\n",
      "          [ 0.4823, -0.3126,  0.0000]]],\n",
      "\n",
      "\n",
      "        [[[ 1.0356, -0.3914,  0.6849],\n",
      "          [-0.9622, -0.6849,  0.5463],\n",
      "          [-0.5708, -0.0326, -0.1468]]],\n",
      "\n",
      "\n",
      "        [[[ 0.1648, -0.3296,  0.0824],\n",
      "          [ 0.9228,  0.8898,  0.5438],\n",
      "          [-2.1091, -0.6097,  0.5273]]],\n",
      "\n",
      "\n",
      "        [[[-0.1709, -0.2020,  0.5593],\n",
      "          [-0.1088,  0.4195,  0.5749],\n",
      "          [ 0.2020,  1.9733, -0.1398]]],\n",
      "\n",
      "\n",
      "        [[[ 0.3595, -0.0282,  0.1269],\n",
      "          [-0.2467, -0.0070, -0.9022],\n",
      "          [-0.5639, -0.0705,  0.0282]]],\n",
      "\n",
      "\n",
      "        [[[-0.0663,  1.5425,  0.6469],\n",
      "          [ 0.3151, -0.3815, -2.1230],\n",
      "          [-0.0166, -0.4976, -1.1610]]],\n",
      "\n",
      "\n",
      "        [[[ 0.1133,  0.3660, -1.0370],\n",
      "          [ 0.8366, -0.3399, -0.4793],\n",
      "          [-0.2091, -0.1046, -0.3224]]],\n",
      "\n",
      "\n",
      "        [[[-0.4703, -0.5729,  0.0428],\n",
      "          [-0.2052,  0.0428, -0.3933],\n",
      "          [-0.0941, -1.0945, -0.9149]]],\n",
      "\n",
      "\n",
      "        [[[-0.4874, -0.1447,  0.0876],\n",
      "          [ 0.0038, -0.2094, -0.1142],\n",
      "          [-0.1219, -0.2704, -0.0381]]],\n",
      "\n",
      "\n",
      "        [[[ 0.7003,  0.0812, -0.1827],\n",
      "          [ 0.4567,  0.8830, -0.0913],\n",
      "          [ 1.2890, -0.9642, -0.0609]]],\n",
      "\n",
      "\n",
      "        [[[ 0.0241, -0.2895,  0.1447],\n",
      "          [-2.9911,  1.2784, -1.2061],\n",
      "          [ 1.5920,  0.0724, -0.3136]]],\n",
      "\n",
      "\n",
      "        [[[ 0.1185,  0.6993, -0.1067],\n",
      "          [ 0.7526,  0.3200,  0.0178],\n",
      "          [-0.0474, -0.5274,  0.2193]]],\n",
      "\n",
      "\n",
      "        [[[-0.3605, -0.1331, -0.2143],\n",
      "          [-0.4157, -0.1559,  0.0779],\n",
      "          [-0.1689,  0.0779, -0.1039]]],\n",
      "\n",
      "\n",
      "        [[[ 0.6708, -0.2911,  0.1012],\n",
      "          [ 0.1139,  1.6073, -0.2405],\n",
      "          [ 0.0506, -0.0127, -0.0127]]],\n",
      "\n",
      "\n",
      "        [[[-0.7969,  0.0464, -0.9284],\n",
      "          [-0.2012,  0.3017,  0.2785],\n",
      "          [-0.0387, -0.1315,  0.0155]]],\n",
      "\n",
      "\n",
      "        [[[-0.4717, -0.6493,  0.0406],\n",
      "          [-0.2283, -0.1725, -0.2688],\n",
      "          [-0.1014,  0.0507,  0.5529]]],\n",
      "\n",
      "\n",
      "        [[[-0.3060, -0.1648,  0.1491],\n",
      "          [-0.6042,  0.0235, -1.0043],\n",
      "          [ 0.3060,  0.3217, -0.2903]]],\n",
      "\n",
      "\n",
      "        [[[-0.3233, -0.4812, -0.0338],\n",
      "          [-0.1955, -0.0263,  0.1203],\n",
      "          [-0.2933,  0.0940, -0.2293]]],\n",
      "\n",
      "\n",
      "        [[[-0.8379, -0.4353,  0.2068],\n",
      "          [ 0.1415, -0.5550, -1.3929],\n",
      "          [-0.1088,  0.1850, -0.0544]]],\n",
      "\n",
      "\n",
      "        [[[ 0.3491,  1.3854,  0.2291],\n",
      "          [ 1.0800,  0.2727, -0.8400],\n",
      "          [-0.5345, -0.5345,  0.5345]]],\n",
      "\n",
      "\n",
      "        [[[-1.2265, -1.2660, -0.2638],\n",
      "          [-0.2506, -0.2638, -0.1714],\n",
      "          [-0.0791,  0.4616,  1.6749]]],\n",
      "\n",
      "\n",
      "        [[[ 0.4402,  0.4549, -0.2054],\n",
      "          [ 0.8071,  1.8637, -0.7631],\n",
      "          [ 0.9832, -0.7337, -0.7631]]],\n",
      "\n",
      "\n",
      "        [[[ 0.1722, -0.6177,  0.0238],\n",
      "          [-0.2494, -0.7483, -0.2138],\n",
      "          [-0.1604, -0.1307,  0.1485]]],\n",
      "\n",
      "\n",
      "        [[[ 0.1748,  0.2225, -0.0636],\n",
      "          [ 0.2384,  0.9378,  0.0397],\n",
      "          [ 0.9855, -0.2384,  0.0556]]],\n",
      "\n",
      "\n",
      "        [[[ 0.7645,  1.0702,  0.0127],\n",
      "          [-0.6625, -1.4397, -0.5861],\n",
      "          [-1.6054, -0.4077,  0.6498]]],\n",
      "\n",
      "\n",
      "        [[[-0.1853, -0.3355, -0.4256],\n",
      "          [-0.2053, -0.1001, -0.5908],\n",
      "          [-0.2904, -0.0150,  0.6259]]],\n",
      "\n",
      "\n",
      "        [[[ 0.6796,  0.4364,  0.7654],\n",
      "          [-0.3362, -0.0858, -0.0930],\n",
      "          [ 0.3505,  0.7440, -0.2647]]],\n",
      "\n",
      "\n",
      "        [[[ 0.6914,  0.6116, -2.6593],\n",
      "          [-1.1169, -0.0532,  3.3774],\n",
      "          [-1.3829, -0.2659,  0.0266]]],\n",
      "\n",
      "\n",
      "        [[[ 1.0329, -0.0407, -0.1627],\n",
      "          [ 0.0000, -0.2928, -0.6344],\n",
      "          [ 0.0325, -0.5530, -0.2115]]],\n",
      "\n",
      "\n",
      "        [[[-0.3416,  0.2420, -0.2562],\n",
      "          [ 0.0427,  1.2810,  0.1423],\n",
      "          [-0.0712,  1.8076,  0.7828]]],\n",
      "\n",
      "\n",
      "        [[[ 0.0237,  0.0947,  0.3031],\n",
      "          [ 0.1089,  0.5163,  0.5684],\n",
      "          [ 0.2889,  0.1942, -0.1753]]]], size=(64, 1, 3, 3),\n",
      "       dtype=torch.qint8, quantization_scheme=torch.per_channel_affine,\n",
      "       scale=tensor([0.0124, 0.0065, 0.0134, 0.0100, 0.0077, 0.0110, 0.0043, 0.0145, 0.0031,\n",
      "        0.0117, 0.0069, 0.0073, 0.0144, 0.0052, 0.0053, 0.0068, 0.0145, 0.0206,\n",
      "        0.0084, 0.0118, 0.0062, 0.0073, 0.0112, 0.0083, 0.0179, 0.0116, 0.0056,\n",
      "        0.0129, 0.0049, 0.0097, 0.0061, 0.0062, 0.0056, 0.0089, 0.0082, 0.0165,\n",
      "        0.0155, 0.0070, 0.0166, 0.0087, 0.0086, 0.0038, 0.0101, 0.0241, 0.0059,\n",
      "        0.0032, 0.0127, 0.0077, 0.0051, 0.0078, 0.0038, 0.0109, 0.0109, 0.0132,\n",
      "        0.0147, 0.0059, 0.0079, 0.0127, 0.0050, 0.0072, 0.0266, 0.0081, 0.0142,\n",
      "        0.0047], dtype=torch.float64),\n",
      "       zero_point=tensor([0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0,\n",
      "        0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0,\n",
      "        0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0]),\n",
      "       axis=0)), ('features.2.block.1.0.bias', Parameter containing:\n",
      "tensor([-0.2901,  0.4711, -1.0682, -0.6882,  0.2618, -0.8925, -0.4128, -1.1245,\n",
      "        -0.4670, -0.0492, -0.1807, -0.9802,  0.1561,  0.4912,  0.5530,  1.0243,\n",
      "        -0.3556,  0.1562, -0.1779,  0.1805, -0.4128,  0.4942,  0.9851, -0.9884,\n",
      "        -1.0974,  1.0827,  0.4181,  0.3673, -0.5704, -0.4613, -0.4820, -0.6247,\n",
      "        -0.3729, -0.0505,  0.2685, -0.1158, -1.0585,  0.3996,  0.7251,  0.3842,\n",
      "         1.4340,  0.4809, -0.6448,  0.5934, -0.5231,  0.4409, -0.6469,  0.4572,\n",
      "         0.4342,  0.4261,  0.5259,  0.7774, -0.6678,  0.4647, -0.7972,  0.6506,\n",
      "        -0.8326,  0.8110,  0.5188, -0.8802,  0.1779,  0.2842, -1.2747, -0.7077])), ('features.2.block.1.0.scale', tensor(0.0964)), ('features.2.block.1.0.zero_point', tensor(0)), ('features.2.block.2.0.weight', tensor([[[[-0.0235]],\n",
      "\n",
      "         [[ 0.1950]],\n",
      "\n",
      "         [[ 0.2420]],\n",
      "\n",
      "         ...,\n",
      "\n",
      "         [[-0.2353]],\n",
      "\n",
      "         [[ 0.1580]],\n",
      "\n",
      "         [[-0.2488]]],\n",
      "\n",
      "\n",
      "        [[[ 0.0285]],\n",
      "\n",
      "         [[ 0.0652]],\n",
      "\n",
      "         [[ 0.2485]],\n",
      "\n",
      "         ...,\n",
      "\n",
      "         [[ 0.3626]],\n",
      "\n",
      "         [[-0.1019]],\n",
      "\n",
      "         [[ 0.2485]]],\n",
      "\n",
      "\n",
      "        [[[-0.3394]],\n",
      "\n",
      "         [[ 0.4469]],\n",
      "\n",
      "         [[ 0.1301]],\n",
      "\n",
      "         ...,\n",
      "\n",
      "         [[-0.3507]],\n",
      "\n",
      "         [[ 0.0735]],\n",
      "\n",
      "         [[-0.2376]]],\n",
      "\n",
      "\n",
      "        ...,\n",
      "\n",
      "\n",
      "        [[[ 0.0854]],\n",
      "\n",
      "         [[ 0.1799]],\n",
      "\n",
      "         [[-0.0854]],\n",
      "\n",
      "         ...,\n",
      "\n",
      "         [[-0.2164]],\n",
      "\n",
      "         [[ 0.0671]],\n",
      "\n",
      "         [[-0.0244]]],\n",
      "\n",
      "\n",
      "        [[[ 0.2264]],\n",
      "\n",
      "         [[ 0.3722]],\n",
      "\n",
      "         [[ 0.0038]],\n",
      "\n",
      "         ...,\n",
      "\n",
      "         [[ 0.0806]],\n",
      "\n",
      "         [[ 0.0422]],\n",
      "\n",
      "         [[-0.0384]]],\n",
      "\n",
      "\n",
      "        [[[ 0.0889]],\n",
      "\n",
      "         [[-0.2613]],\n",
      "\n",
      "         [[-0.0261]],\n",
      "\n",
      "         ...,\n",
      "\n",
      "         [[-0.3084]],\n",
      "\n",
      "         [[ 0.0157]],\n",
      "\n",
      "         [[ 0.0418]]]], size=(24, 64, 1, 1), dtype=torch.qint8,\n",
      "       quantization_scheme=torch.per_channel_affine,\n",
      "       scale=tensor([0.0034, 0.0041, 0.0057, 0.0053, 0.0044, 0.0044, 0.0042, 0.0041, 0.0036,\n",
      "        0.0057, 0.0043, 0.0042, 0.0035, 0.0044, 0.0046, 0.0040, 0.0031, 0.0045,\n",
      "        0.0041, 0.0044, 0.0024, 0.0030, 0.0038, 0.0052], dtype=torch.float64),\n",
      "       zero_point=tensor([0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0]),\n",
      "       axis=0)), ('features.2.block.2.0.bias', Parameter containing:\n",
      "tensor([-0.5131, -0.1758,  0.6096,  0.3962, -1.0678, -0.4486, -0.3034,  0.7239,\n",
      "        -0.0254, -0.2494, -0.1268, -0.8169, -0.1822,  0.2095, -0.2601,  0.0305,\n",
      "        -1.2837,  0.8005, -1.0643,  1.4347, -0.0337, -0.3518, -1.1195,  0.4918])), ('features.2.block.2.0.scale', tensor(0.1473)), ('features.2.block.2.0.zero_point', tensor(60)), ('features.2.skip_add.scale', tensor(1.)), ('features.2.skip_add.zero_point', tensor(0)), ('features.3.block.0.0.weight', tensor([[[[-0.0684]],\n",
      "\n",
      "         [[ 0.2379]],\n",
      "\n",
      "         [[-0.0595]],\n",
      "\n",
      "         ...,\n",
      "\n",
      "         [[-0.0892]],\n",
      "\n",
      "         [[-0.2438]],\n",
      "\n",
      "         [[ 0.0535]]],\n",
      "\n",
      "\n",
      "        [[[ 0.0690]],\n",
      "\n",
      "         [[ 0.1543]],\n",
      "\n",
      "         [[ 0.0853]],\n",
      "\n",
      "         ...,\n",
      "\n",
      "         [[-0.2720]],\n",
      "\n",
      "         [[-0.1908]],\n",
      "\n",
      "         [[ 0.5156]]],\n",
      "\n",
      "\n",
      "        [[[-0.0603]],\n",
      "\n",
      "         [[-0.1567]],\n",
      "\n",
      "         [[ 0.0281]],\n",
      "\n",
      "         ...,\n",
      "\n",
      "         [[ 0.2250]],\n",
      "\n",
      "         [[ 0.3014]],\n",
      "\n",
      "         [[-0.1567]]],\n",
      "\n",
      "\n",
      "        ...,\n",
      "\n",
      "\n",
      "        [[[ 0.0420]],\n",
      "\n",
      "         [[ 0.1204]],\n",
      "\n",
      "         [[ 0.1316]],\n",
      "\n",
      "         ...,\n",
      "\n",
      "         [[-0.1008]],\n",
      "\n",
      "         [[ 0.3135]],\n",
      "\n",
      "         [[ 0.2239]]],\n",
      "\n",
      "\n",
      "        [[[ 0.1781]],\n",
      "\n",
      "         [[ 0.0111]],\n",
      "\n",
      "         [[ 0.0408]],\n",
      "\n",
      "         ...,\n",
      "\n",
      "         [[ 0.0148]],\n",
      "\n",
      "         [[-0.0927]],\n",
      "\n",
      "         [[-0.0371]]],\n",
      "\n",
      "\n",
      "        [[[ 0.1950]],\n",
      "\n",
      "         [[ 0.2911]],\n",
      "\n",
      "         [[-0.2881]],\n",
      "\n",
      "         ...,\n",
      "\n",
      "         [[-0.3811]],\n",
      "\n",
      "         [[-0.2100]],\n",
      "\n",
      "         [[ 0.1020]]]], size=(72, 24, 1, 1), dtype=torch.qint8,\n",
      "       quantization_scheme=torch.per_channel_affine,\n",
      "       scale=tensor([0.0030, 0.0041, 0.0040, 0.0050, 0.0045, 0.0026, 0.0060, 0.0037, 0.0050,\n",
      "        0.0027, 0.0031, 0.0059, 0.0039, 0.0031, 0.0037, 0.0034, 0.0053, 0.0032,\n",
      "        0.0039, 0.0028, 0.0037, 0.0026, 0.0026, 0.0032, 0.0034, 0.0032, 0.0025,\n",
      "        0.0025, 0.0038, 0.0025, 0.0033, 0.0031, 0.0036, 0.0021, 0.0030, 0.0041,\n",
      "        0.0046, 0.0034, 0.0049, 0.0028, 0.0044, 0.0026, 0.0042, 0.0036, 0.0038,\n",
      "        0.0039, 0.0026, 0.0039, 0.0030, 0.0026, 0.0038, 0.0033, 0.0026, 0.0024,\n",
      "        0.0038, 0.0031, 0.0034, 0.0036, 0.0027, 0.0046, 0.0038, 0.0031, 0.0049,\n",
      "        0.0065, 0.0042, 0.0025, 0.0042, 0.0029, 0.0026, 0.0028, 0.0037, 0.0030],\n",
      "       dtype=torch.float64),\n",
      "       zero_point=tensor([0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0,\n",
      "        0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0,\n",
      "        0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0]),\n",
      "       axis=0)), ('features.3.block.0.0.bias', Parameter containing:\n",
      "tensor([ 0.0257,  0.0739, -0.0624, -0.0022,  0.0061, -0.0729,  0.1007, -0.0186,\n",
      "        -0.0416,  0.0468,  0.0803,  0.0339, -0.0092, -0.0703,  0.0030, -0.0536,\n",
      "        -0.0368,  0.0173, -0.0715,  0.0062,  0.0627, -0.0074, -0.0363, -0.0892,\n",
      "         0.0080,  0.0234,  0.0477, -0.0077,  0.0937, -0.0404,  0.0640,  0.0082,\n",
      "        -0.0207, -0.0160, -0.0378,  0.0204,  0.0623, -0.0122,  0.0558, -0.0345,\n",
      "        -0.0841,  0.0047,  0.0023, -0.0680, -0.0357,  0.0526,  0.0083, -0.0059,\n",
      "         0.0173,  0.0413,  0.0175,  0.0574, -0.0568,  0.0088, -0.0169, -0.0175,\n",
      "         0.0035,  0.0743,  0.0158,  0.0546, -0.0741,  0.1247,  0.0466, -0.0020,\n",
      "        -0.0232, -0.0507,  0.0532,  0.0413, -0.0350,  0.0142,  0.0121,  0.0237])), ('features.3.block.0.0.scale', tensor(0.0735)), ('features.3.block.0.0.zero_point', tensor(0)), ('features.3.block.1.0.weight', tensor([[[[-0.2250,  0.8405, -0.4699],\n",
      "          [-0.8471, -0.4103, -0.5427],\n",
      "          [ 0.0927, -0.6089, -0.0794]]],\n",
      "\n",
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      "        [[[-0.2884,  0.0085, -0.1951],\n",
      "          [ 1.0771,  0.1696, -0.2036],\n",
      "          [-0.5089,  0.9499,  0.4156]]],\n",
      "\n",
      "\n",
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      "          [-0.3790, -0.5869, -0.5502],\n",
      "          [ 1.5529, -0.0978,  0.4157]]],\n",
      "\n",
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      "\n",
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      "          [ 1.0987, -1.9776,  0.0000]]],\n",
      "\n",
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      "\n",
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      "\n",
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      "\n",
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      "\n",
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      "\n",
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      "\n",
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      "\n",
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      "\n",
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      "\n",
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      "\n",
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      "\n",
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      "\n",
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      "          [-0.4868, -0.3895, -0.4691]]],\n",
      "\n",
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      "\n",
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      "\n",
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      "          [ 0.3147,  0.7342, -1.9179],\n",
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      "\n",
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      "          [ 0.4562,  0.2548,  0.0593],\n",
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      "\n",
      "\n",
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      "          [-0.2879,  0.5257,  0.4506],\n",
      "          [ 0.0501,  0.0876,  0.8261]]],\n",
      "\n",
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      "          [ 0.0724,  0.1689,  0.4537],\n",
      "          [-0.0483,  0.0000,  0.6130]]],\n",
      "\n",
      "\n",
      "        [[[ 0.0751,  0.0657, -1.1544],\n",
      "          [ 0.4223, -0.0094,  0.9948],\n",
      "          [-0.8447,  0.3003,  0.5537]]],\n",
      "\n",
      "\n",
      "        [[[-0.1289, -0.2344, -1.5004],\n",
      "          [-0.9495, -0.1641,  0.2462],\n",
      "          [ 0.5158, -0.0234,  0.4689]]],\n",
      "\n",
      "\n",
      "        [[[-0.1929,  0.0738,  0.3121],\n",
      "          [ 0.5787, -0.1248,  0.0738],\n",
      "          [ 0.7149,  0.3234, -0.0340]]],\n",
      "\n",
      "\n",
      "        [[[-0.0423,  0.2707, -0.6937],\n",
      "          [-0.1523,  0.0085, -1.0829],\n",
      "          [ 0.4315, -0.1354,  0.3384]]],\n",
      "\n",
      "\n",
      "        [[[-0.1078, -0.5796,  0.4853],\n",
      "          [-1.7253,  0.7548,  1.0649],\n",
      "          [ 0.0539, -0.1483,  0.8627]]],\n",
      "\n",
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      "          [-0.2964, -0.8430,  0.1186],\n",
      "          [-0.0659, -0.1054, -0.0790]]],\n",
      "\n",
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      "        [[[-0.1591,  0.6576,  0.4879],\n",
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      "\n",
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      "        [[[-0.2310,  0.0231, -0.9855],\n",
      "          [-0.2156,  0.0616, -0.0462],\n",
      "          [-0.5082, -0.2387, -0.3850]]],\n",
      "\n",
      "\n",
      "        [[[ 0.4826, -0.1778,  0.0254],\n",
      "          [ 0.0085, -1.0838,  0.0085],\n",
      "          [ 0.2286,  0.3810,  0.5758]]],\n",
      "\n",
      "\n",
      "        [[[ 0.6383,  0.8357,  0.0921],\n",
      "          [-0.0329, -0.0790, -0.0592],\n",
      "          [ 0.0790,  0.1184,  0.0658]]],\n",
      "\n",
      "\n",
      "        [[[-0.1271, -0.2542,  0.2542],\n",
      "          [-0.6694, -0.1864, -0.3643],\n",
      "          [-0.1949,  0.1610, -1.0845]]],\n",
      "\n",
      "\n",
      "        [[[ 0.3130,  0.3942,  0.7652],\n",
      "          [-0.3594,  0.1043, -0.5333],\n",
      "          [-0.1159, -0.4869, -1.4840]]],\n",
      "\n",
      "\n",
      "        [[[ 0.0954,  0.3891,  0.1835],\n",
      "          [-0.9397,  0.9323, -0.0294],\n",
      "          [ 0.0000,  0.6093,  0.1615]]],\n",
      "\n",
      "\n",
      "        [[[-1.4179,  0.4283,  0.3102],\n",
      "          [ 0.7681, -0.3988,  0.2363],\n",
      "          [ 1.5656,  0.7681, -1.8906]]],\n",
      "\n",
      "\n",
      "        [[[ 0.1581,  0.3121,  0.1123],\n",
      "          [ 0.0832,  0.5284,  0.1207],\n",
      "          [-0.2288,  0.2247,  0.2705]]],\n",
      "\n",
      "\n",
      "        [[[-0.2237, -0.4909, -0.7705],\n",
      "          [-0.2485, -0.7580, -0.0932],\n",
      "          [-0.3604, -0.1056, -0.1056]]],\n",
      "\n",
      "\n",
      "        [[[ 0.1641, -0.0259, -0.1641],\n",
      "          [ 0.2461,  0.0907,  0.5483],\n",
      "          [ 0.2850,  0.1684,  0.3583]]],\n",
      "\n",
      "\n",
      "        [[[ 0.2566, -0.1645, -0.1579],\n",
      "          [-0.2171,  0.8355, -0.1842],\n",
      "          [ 0.4210,  0.7697,  0.0263]]],\n",
      "\n",
      "\n",
      "        [[[ 0.2055,  0.3627,  0.0604],\n",
      "          [ 0.4835,  0.1451,  0.4835],\n",
      "          [-0.0846, -1.5231, -0.2659]]],\n",
      "\n",
      "\n",
      "        [[[-0.5016,  0.1157, -1.0932],\n",
      "          [-0.0514, -0.4373,  0.3215],\n",
      "          [-0.2444,  0.5916, -1.6462]]],\n",
      "\n",
      "\n",
      "        [[[-1.2814, -0.0115,  0.7619],\n",
      "          [ 0.3463,  0.3694,  0.4733],\n",
      "          [ 0.3463, -1.4777, -0.2540]]],\n",
      "\n",
      "\n",
      "        [[[ 0.1180,  0.2301,  0.1534],\n",
      "          [ 0.2183,  0.7492,  0.1711],\n",
      "          [ 0.1298,  0.0059, -0.2065]]],\n",
      "\n",
      "\n",
      "        [[[-0.0636, -0.2719, -0.2372],\n",
      "          [ 0.1620, -0.2604, -0.3935],\n",
      "          [-0.6423,  0.0000, -0.1389]]],\n",
      "\n",
      "\n",
      "        [[[ 0.6569,  0.8100,  0.1722],\n",
      "          [-0.2487,  0.2806, -0.2870],\n",
      "          [-0.1467,  0.1722, -0.1212]]],\n",
      "\n",
      "\n",
      "        [[[ 0.1601,  0.2107, -1.0789],\n",
      "          [-0.1349, -0.1939, -0.7670],\n",
      "          [ 0.1433,  0.4130,  0.1517]]],\n",
      "\n",
      "\n",
      "        [[[ 0.1136, -0.0310,  0.7436],\n",
      "          [ 0.6507, -0.0620,  0.6920],\n",
      "          [-1.0432,  0.0310, -1.3220]]],\n",
      "\n",
      "\n",
      "        [[[ 0.1496, -0.0551,  0.9293],\n",
      "          [-0.1496,  0.1418, -0.4016],\n",
      "          [-0.7639, -0.2835, -0.7482]]],\n",
      "\n",
      "\n",
      "        [[[ 0.9505, -0.3480,  0.1528],\n",
      "          [-0.7638, -0.5686, -0.0424],\n",
      "          [-0.7129, -0.0170,  0.4838]]],\n",
      "\n",
      "\n",
      "        [[[ 0.6217,  0.1665, -0.1887],\n",
      "          [-0.4330,  0.2109, -0.0444],\n",
      "          [-0.2220,  1.1102, -1.4100]]],\n",
      "\n",
      "\n",
      "        [[[-0.1557,  0.6509,  0.0142],\n",
      "          [-0.6368, -0.5094,  0.0708],\n",
      "          [ 1.7972,  0.0283, -0.2406]]],\n",
      "\n",
      "\n",
      "        [[[ 0.2653,  0.7769,  0.4074],\n",
      "          [ 0.1895,  0.1326, -0.1611],\n",
      "          [ 0.2842, -0.2463, -1.2127]]],\n",
      "\n",
      "\n",
      "        [[[-0.3097, -0.1337, -0.7319],\n",
      "          [-0.3519, -0.4082, -0.3589],\n",
      "          [-0.1548, -0.8867,  0.2322]]],\n",
      "\n",
      "\n",
      "        [[[-0.2184, -0.0660, -0.6500],\n",
      "          [-0.0152, -0.2488,  0.0711],\n",
      "          [-0.1117, -0.0609, -0.1219]]],\n",
      "\n",
      "\n",
      "        [[[ 0.4154,  0.1984,  0.0620],\n",
      "          [ 0.3534,  0.1922,  0.7874],\n",
      "          [ 0.3472, -0.3968, -0.2232]]],\n",
      "\n",
      "\n",
      "        [[[ 0.1089,  0.1490,  0.0802],\n",
      "          [ 0.0172, -0.0057, -0.1261],\n",
      "          [ 0.1490,  0.4355,  0.7278]]],\n",
      "\n",
      "\n",
      "        [[[ 0.1694,  0.4177, -1.4451],\n",
      "          [-0.3161,  0.0339, -0.5081],\n",
      "          [ 0.1016,  0.2935,  0.0565]]],\n",
      "\n",
      "\n",
      "        [[[-0.6228,  0.3583, -0.2590],\n",
      "          [-0.6338,  0.1653,  0.0276],\n",
      "          [-0.0331, -0.4575, -0.0551]]]], size=(72, 1, 3, 3),\n",
      "       dtype=torch.qint8, quantization_scheme=torch.per_channel_affine,\n",
      "       scale=tensor([0.0066, 0.0085, 0.0122, 0.0103, 0.0157, 0.0114, 0.0071, 0.0100, 0.0099,\n",
      "        0.0128, 0.0082, 0.0102, 0.0124, 0.0106, 0.0078, 0.0044, 0.0051, 0.0096,\n",
      "        0.0112, 0.0063, 0.0114, 0.0083, 0.0120, 0.0076, 0.0061, 0.0115, 0.0193,\n",
      "        0.0078, 0.0089, 0.0178, 0.0131, 0.0150, 0.0059, 0.0125, 0.0048, 0.0094,\n",
      "        0.0117, 0.0057, 0.0085, 0.0135, 0.0066, 0.0106, 0.0077, 0.0085, 0.0066,\n",
      "        0.0085, 0.0116, 0.0073, 0.0148, 0.0042, 0.0062, 0.0043, 0.0066, 0.0121,\n",
      "        0.0129, 0.0115, 0.0059, 0.0058, 0.0064, 0.0084, 0.0103, 0.0079, 0.0085,\n",
      "        0.0111, 0.0142, 0.0095, 0.0070, 0.0051, 0.0062, 0.0057, 0.0113, 0.0055],\n",
      "       dtype=torch.float64),\n",
      "       zero_point=tensor([0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0,\n",
      "        0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0,\n",
      "        0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0]),\n",
      "       axis=0)), ('features.3.block.1.0.bias', Parameter containing:\n",
      "tensor([ 0.8575, -0.4562, -0.0256,  0.7992, -0.1300, -0.0542,  0.7656, -0.2800,\n",
      "        -0.4193,  1.1763, -0.4862, -0.0663, -0.7968, -0.3139,  0.6153,  0.3004,\n",
      "        -0.4810,  0.3824, -0.1249,  0.3042, -0.0531,  0.3808,  0.1911,  0.3871,\n",
      "        -0.2745,  0.3186, -0.0705, -0.9064,  0.8298, -0.3665, -0.3957, -0.1711,\n",
      "        -0.5909, -0.7620, -0.6277, -0.0366,  0.5265, -0.4909,  0.3483, -0.1954,\n",
      "         0.5570, -0.0439,  1.0016, -0.1503, -0.6302,  1.0846,  0.5273, -0.5225,\n",
      "        -0.1639, -0.5049,  1.2925, -0.4980, -0.5107,  0.0334,  0.7545,  0.3345,\n",
      "        -0.4853,  0.7308, -0.4325,  0.4211, -0.0507,  0.4498,  0.1730,  0.0307,\n",
      "        -0.3685, -0.1249,  1.1563,  0.4669, -0.5158, -0.4489,  0.3825,  0.5542])), ('features.3.block.1.0.scale', tensor(0.0999)), ('features.3.block.1.0.zero_point', tensor(0)), ('features.3.block.2.0.weight', tensor([[[[ 0.1965]],\n",
      "\n",
      "         [[-0.1119]],\n",
      "\n",
      "         [[-0.1462]],\n",
      "\n",
      "         ...,\n",
      "\n",
      "         [[-0.0777]],\n",
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      "         [[ 0.2901]],\n",
      "\n",
      "         [[ 0.0982]]],\n",
      "\n",
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      "        [[[-0.0209]],\n",
      "\n",
      "         [[-0.2580]],\n",
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      "         [[-0.1011]],\n",
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      "         ...,\n",
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      "         [[ 0.1394]],\n",
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      "         [[ 0.0732]],\n",
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      "         [[ 0.0000]]],\n",
      "\n",
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      "        [[[ 0.1473]],\n",
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      "         [[ 0.1473]],\n",
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      "         [[ 0.0340]],\n",
      "\n",
      "         ...,\n",
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      "         [[-0.1549]],\n",
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      "         [[ 0.1247]],\n",
      "\n",
      "         [[-0.0113]]],\n",
      "\n",
      "\n",
      "        ...,\n",
      "\n",
      "\n",
      "        [[[ 0.3161]],\n",
      "\n",
      "         [[-0.4138]],\n",
      "\n",
      "         [[-0.0130]],\n",
      "\n",
      "         ...,\n",
      "\n",
      "         [[-0.1369]],\n",
      "\n",
      "         [[ 0.1531]],\n",
      "\n",
      "         [[ 0.0554]]],\n",
      "\n",
      "\n",
      "        [[[-0.2549]],\n",
      "\n",
      "         [[-0.2190]],\n",
      "\n",
      "         [[-0.2297]],\n",
      "\n",
      "         ...,\n",
      "\n",
      "         [[-0.1113]],\n",
      "\n",
      "         [[-0.0754]],\n",
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      "         [[ 0.0431]]],\n",
      "\n",
      "\n",
      "        [[[-0.1757]],\n",
      "\n",
      "         [[ 0.1337]],\n",
      "\n",
      "         [[-0.0497]],\n",
      "\n",
      "         ...,\n",
      "\n",
      "         [[-0.0573]],\n",
      "\n",
      "         [[-0.1872]],\n",
      "\n",
      "         [[-0.2674]]]], size=(24, 72, 1, 1), dtype=torch.qint8,\n",
      "       quantization_scheme=torch.per_channel_affine,\n",
      "       scale=tensor([0.0023, 0.0035, 0.0038, 0.0033, 0.0034, 0.0031, 0.0042, 0.0034, 0.0033,\n",
      "        0.0032, 0.0040, 0.0034, 0.0033, 0.0044, 0.0033, 0.0042, 0.0021, 0.0028,\n",
      "        0.0041, 0.0030, 0.0060, 0.0033, 0.0036, 0.0038], dtype=torch.float64),\n",
      "       zero_point=tensor([0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0]),\n",
      "       axis=0)), ('features.3.block.2.0.bias', Parameter containing:\n",
      "tensor([ 0.3208,  0.8246, -1.2798,  0.6363, -0.2751,  0.8620,  0.5545, -0.1222,\n",
      "         1.0306,  0.3200,  0.1138, -0.1234, -0.1727,  0.9017,  0.2339,  0.4599,\n",
      "         0.6198, -0.8029, -0.4975,  0.2978,  0.9103, -0.3718,  0.1336,  0.0176])), ('features.3.block.2.0.scale', tensor(0.1482)), ('features.3.block.2.0.zero_point', tensor(64)), ('features.3.skip_add.scale', tensor(0.1897)), ('features.3.skip_add.zero_point', tensor(60)), ('features.4.block.0.0.weight', tensor([[[[-0.0026]],\n",
      "\n",
      "         [[ 0.1386]],\n",
      "\n",
      "         [[ 0.1181]],\n",
      "\n",
      "         ...,\n",
      "\n",
      "         [[-0.3029]],\n",
      "\n",
      "         [[ 0.1437]],\n",
      "\n",
      "         [[ 0.1206]]],\n",
      "\n",
      "\n",
      "        [[[ 0.0729]],\n",
      "\n",
      "         [[ 0.0326]],\n",
      "\n",
      "         [[ 0.1189]],\n",
      "\n",
      "         ...,\n",
      "\n",
      "         [[-0.2435]],\n",
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      "         [[ 0.0729]],\n",
      "\n",
      "         [[-0.1706]]],\n",
      "\n",
      "\n",
      "        [[[ 0.0510]],\n",
      "\n",
      "         [[-0.1722]],\n",
      "\n",
      "         [[-0.3126]],\n",
      "\n",
      "         ...,\n",
      "\n",
      "         [[ 0.1690]],\n",
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      "         [[-0.1658]],\n",
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      "         [[ 0.0447]]],\n",
      "\n",
      "\n",
      "        ...,\n",
      "\n",
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      "        [[[-0.0300]],\n",
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      "         [[-0.1663]],\n",
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      "         [[-0.0139]],\n",
      "\n",
      "         ...,\n",
      "\n",
      "         [[-0.0116]],\n",
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      "         [[-0.0670]],\n",
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      "         [[ 0.1201]]],\n",
      "\n",
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      "        [[[-0.0864]],\n",
      "\n",
      "         [[ 0.0842]],\n",
      "\n",
      "         [[ 0.0864]],\n",
      "\n",
      "         ...,\n",
      "\n",
      "         [[ 0.0044]],\n",
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      "         [[-0.0332]],\n",
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      "         [[ 0.1307]]],\n",
      "\n",
      "\n",
      "        [[[-0.0212]],\n",
      "\n",
      "         [[ 0.1180]],\n",
      "\n",
      "         [[-0.0878]],\n",
      "\n",
      "         ...,\n",
      "\n",
      "         [[ 0.0848]],\n",
      "\n",
      "         [[-0.2119]],\n",
      "\n",
      "         [[ 0.2270]]]], size=(72, 24, 1, 1), dtype=torch.qint8,\n",
      "       quantization_scheme=torch.per_channel_affine,\n",
      "       scale=tensor([0.0026, 0.0019, 0.0032, 0.0033, 0.0024, 0.0028, 0.0044, 0.0017, 0.0021,\n",
      "        0.0024, 0.0026, 0.0023, 0.0022, 0.0019, 0.0024, 0.0040, 0.0020, 0.0025,\n",
      "        0.0025, 0.0029, 0.0035, 0.0019, 0.0023, 0.0022, 0.0023, 0.0035, 0.0023,\n",
      "        0.0019, 0.0028, 0.0024, 0.0023, 0.0024, 0.0025, 0.0025, 0.0026, 0.0020,\n",
      "        0.0022, 0.0021, 0.0031, 0.0020, 0.0023, 0.0024, 0.0019, 0.0028, 0.0027,\n",
      "        0.0017, 0.0016, 0.0021, 0.0019, 0.0013, 0.0037, 0.0029, 0.0030, 0.0038,\n",
      "        0.0029, 0.0024, 0.0022, 0.0015, 0.0029, 0.0024, 0.0020, 0.0022, 0.0017,\n",
      "        0.0030, 0.0032, 0.0020, 0.0021, 0.0026, 0.0024, 0.0023, 0.0022, 0.0030],\n",
      "       dtype=torch.float64),\n",
      "       zero_point=tensor([0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0,\n",
      "        0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0,\n",
      "        0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0]),\n",
      "       axis=0)), ('features.4.block.0.0.bias', Parameter containing:\n",
      "tensor([ 0.0097, -0.0292,  0.0338,  0.0826,  0.0676, -0.0211,  0.0143, -0.1068,\n",
      "         0.1275,  0.0101,  0.0498,  0.0559, -0.0387,  0.0970, -0.0184,  0.0440,\n",
      "        -0.0059, -0.0455,  0.0697, -0.0030, -0.0402,  0.0197, -0.0604,  0.0792,\n",
      "         0.1167, -0.0301, -0.0107,  0.0655,  0.0990, -0.0238, -0.0250, -0.0880,\n",
      "         0.0769, -0.0476, -0.0103, -0.0066, -0.0522, -0.0655,  0.0615, -0.0288,\n",
      "         0.0570,  0.0268, -0.0677,  0.0268, -0.1361, -0.0779,  0.0577,  0.0876,\n",
      "         0.0216, -0.0460, -0.0380, -0.0333, -0.0321,  0.0466, -0.0140, -0.1056,\n",
      "         0.1434, -0.0101,  0.0693,  0.0130,  0.0410,  0.0367,  0.0148,  0.0338,\n",
      "         0.0215,  0.0272,  0.1603,  0.0431, -0.0369, -0.0346,  0.0135,  0.0080])), ('features.4.block.0.0.scale', tensor(0.0710)), ('features.4.block.0.0.zero_point', tensor(0)), ('features.4.block.1.0.weight', tensor([[[[ 0.0402, -0.0549, -0.1061, -0.3146, -0.0622],\n",
      "          [ 0.0037, -0.1098, -0.0622, -0.0841, -0.0988],\n",
      "          [ 0.1024, -0.0659,  0.0732, -0.4683, -0.3988],\n",
      "          [ 0.2561, -0.0037,  0.0622, -0.3439, -0.1537],\n",
      "          [ 0.0732, -0.0293,  0.0805,  0.0841, -0.1829]]],\n",
      "\n",
      "\n",
      "        [[[-0.1459, -0.0973, -0.0208, -0.1876, -0.2015],\n",
      "          [-0.5420, -0.3127, -0.3266, -0.8617, -0.0208],\n",
      "          [-0.2293, -0.1181, -0.1112, -0.1598,  0.0000],\n",
      "          [-0.1390, -0.2710, -0.1737,  0.3822, -0.1459],\n",
      "          [ 0.0973,  0.2154, -0.7992, -0.0139, -0.2849]]],\n",
      "\n",
      "\n",
      "        [[[-0.0653, -0.1142, -0.4486, -0.0082,  0.0979],\n",
      "          [ 0.8157, -0.4242, -0.3426,  0.9707,  0.1060],\n",
      "          [ 0.0816, -0.4242,  0.1550, -0.1224, -0.0979],\n",
      "          [-0.1713, -0.2529,  0.1305, -0.0571,  0.1631],\n",
      "          [-0.2692,  0.5873,  0.0979,  0.3018,  0.2610]]],\n",
      "\n",
      "\n",
      "        ...,\n",
      "\n",
      "\n",
      "        [[[-0.0346, -0.1600, -0.1254, -0.2768, -0.0735],\n",
      "          [ 0.0822, -0.1211, -0.0735,  0.0908, -0.3720],\n",
      "          [-0.2509, -0.1946, -0.0389, -0.0952, -0.0260],\n",
      "          [ 0.1600,  0.3157,  0.0692,  0.1341,  0.0995],\n",
      "          [ 0.5493, -0.4498,  0.1514,  0.0649,  0.1514]]],\n",
      "\n",
      "\n",
      "        [[[ 0.0425,  0.0000, -0.0755,  0.1651,  0.5001],\n",
      "          [ 0.0944,  0.5992,  0.0613, -0.0236, -0.1840],\n",
      "          [ 0.1085,  0.1179,  0.0991,  0.0189,  0.1321],\n",
      "          [ 0.0896, -0.0094,  0.0613,  0.2783,  0.1698],\n",
      "          [-0.4246,  0.0613, -0.1085, -0.1085,  0.1274]]],\n",
      "\n",
      "\n",
      "        [[[-0.2693,  0.2245,  0.1475,  0.1732,  0.3848],\n",
      "          [-0.3271,  0.2052,  0.0257, -0.3463,  0.8144],\n",
      "          [ 0.0321, -0.1603, -0.1475, -0.3271, -0.1283],\n",
      "          [-0.2437, -0.2309, -0.4040, -0.2886, -0.5323],\n",
      "          [-0.2245, -0.1860, -0.0641, -0.1539, -0.0962]]]], size=(72, 1, 5, 5),\n",
      "       dtype=torch.qint8, quantization_scheme=torch.per_channel_affine,\n",
      "       scale=tensor([0.0037, 0.0069, 0.0082, 0.0039, 0.0093, 0.0050, 0.0071, 0.0030, 0.0073,\n",
      "        0.0062, 0.0032, 0.0072, 0.0051, 0.0074, 0.0040, 0.0055, 0.0092, 0.0065,\n",
      "        0.0026, 0.0077, 0.0068, 0.0087, 0.0049, 0.0057, 0.0034, 0.0056, 0.0038,\n",
      "        0.0034, 0.0056, 0.0024, 0.0038, 0.0054, 0.0041, 0.0066, 0.0068, 0.0040,\n",
      "        0.0033, 0.0048, 0.0039, 0.0059, 0.0029, 0.0036, 0.0100, 0.0052, 0.0055,\n",
      "        0.0043, 0.0027, 0.0077, 0.0022, 0.0030, 0.0061, 0.0029, 0.0064, 0.0058,\n",
      "        0.0055, 0.0040, 0.0040, 0.0031, 0.0068, 0.0054, 0.0050, 0.0042, 0.0026,\n",
      "        0.0066, 0.0063, 0.0029, 0.0047, 0.0065, 0.0055, 0.0043, 0.0047, 0.0064],\n",
      "       dtype=torch.float64),\n",
      "       zero_point=tensor([0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0,\n",
      "        0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0,\n",
      "        0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0]),\n",
      "       axis=0)), ('features.4.block.1.0.bias', Parameter containing:\n",
      "tensor([ 0.5458,  1.4622, -0.4474,  1.0485,  0.4990,  0.7276, -0.9773, -0.5948,\n",
      "        -0.1958,  1.3775, -0.9905,  0.3949, -0.1785, -1.2018,  0.9134,  0.0617,\n",
      "         0.8493, -0.5826,  0.7169,  0.3790,  0.4004, -0.1760, -0.3863,  1.4207,\n",
      "         0.9318,  0.4754, -0.8545,  0.5520,  0.6810,  0.7200, -0.6649, -0.6283,\n",
      "         0.9176, -0.1630, -0.3868,  0.5528, -0.4506,  0.6463,  0.7791,  0.4986,\n",
      "        -0.5088,  0.8811,  0.7997,  0.4712, -0.1473, -0.6291,  0.6592, -0.3610,\n",
      "        -0.6406,  0.7710, -0.4018,  1.0281, -0.9370, -0.5317, -0.1415, -0.8856,\n",
      "         0.6408,  0.2441, -0.9731,  1.1035,  0.4306,  0.7734, -0.5803, -0.2276,\n",
      "        -0.2808,  0.6525,  0.1918, -0.0681, -0.4136,  0.1765, -0.5693,  0.8240])), ('features.4.block.1.0.scale', tensor(0.0980)), ('features.4.block.1.0.zero_point', tensor(0)), ('features.4.block.2.fc1.weight', tensor([[[[ 0.1477]],\n",
      "\n",
      "         [[ 0.1611]],\n",
      "\n",
      "         [[ 0.2014]],\n",
      "\n",
      "         ...,\n",
      "\n",
      "         [[ 0.0671]],\n",
      "\n",
      "         [[-0.2148]],\n",
      "\n",
      "         [[-0.3558]]],\n",
      "\n",
      "\n",
      "        [[[ 0.0697]],\n",
      "\n",
      "         [[ 0.3624]],\n",
      "\n",
      "         [[ 0.5017]],\n",
      "\n",
      "         ...,\n",
      "\n",
      "         [[-0.1812]],\n",
      "\n",
      "         [[ 0.1185]],\n",
      "\n",
      "         [[ 0.0906]]],\n",
      "\n",
      "\n",
      "        [[[ 0.4086]],\n",
      "\n",
      "         [[-0.4438]],\n",
      "\n",
      "         [[-0.2536]],\n",
      "\n",
      "         ...,\n",
      "\n",
      "         [[ 0.2113]],\n",
      "\n",
      "         [[ 0.3311]],\n",
      "\n",
      "         [[ 0.1620]]],\n",
      "\n",
      "\n",
      "        ...,\n",
      "\n",
      "\n",
      "        [[[-0.7937]],\n",
      "\n",
      "         [[ 0.0558]],\n",
      "\n",
      "         [[ 0.2790]],\n",
      "\n",
      "         ...,\n",
      "\n",
      "         [[ 0.0558]],\n",
      "\n",
      "         [[-0.4278]],\n",
      "\n",
      "         [[-0.7627]]],\n",
      "\n",
      "\n",
      "        [[[-0.1509]],\n",
      "\n",
      "         [[-0.0934]],\n",
      "\n",
      "         [[-0.0144]],\n",
      "\n",
      "         ...,\n",
      "\n",
      "         [[-0.0647]],\n",
      "\n",
      "         [[-0.1652]],\n",
      "\n",
      "         [[-0.1221]]],\n",
      "\n",
      "\n",
      "        [[[ 0.6000]],\n",
      "\n",
      "         [[-0.0048]],\n",
      "\n",
      "         [[ 0.1524]],\n",
      "\n",
      "         ...,\n",
      "\n",
      "         [[-0.4857]],\n",
      "\n",
      "         [[ 0.0667]],\n",
      "\n",
      "         [[-0.1333]]]], size=(24, 72, 1, 1), dtype=torch.qint8,\n",
      "       quantization_scheme=torch.per_channel_affine,\n",
      "       scale=tensor([0.0067, 0.0070, 0.0070, 0.0055, 0.0048, 0.0057, 0.0071, 0.0080, 0.0078,\n",
      "        0.0053, 0.0077, 0.0071, 0.0054, 0.0074, 0.0060, 0.0052, 0.0070, 0.0055,\n",
      "        0.0056, 0.0070, 0.0069, 0.0062, 0.0072, 0.0048], dtype=torch.float64),\n",
      "       zero_point=tensor([0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0]),\n",
      "       axis=0)), ('features.4.block.2.fc1.bias', Parameter containing:\n",
      "tensor([ 0.0185,  0.0144,  0.0042,  0.0005, -0.0099,  0.0000,  0.0000,  0.0014,\n",
      "        -0.0060, -0.0309, -0.0210, -0.0285,  0.0304,  0.0048, -0.0096, -0.0331,\n",
      "        -0.0376, -0.0194, -0.0136,  0.0084, -0.0012, -0.0168, -0.0093, -0.0311],\n",
      "       requires_grad=True)), ('features.4.block.2.fc1.scale', tensor(0.0171)), ('features.4.block.2.fc1.zero_point', tensor(0)), ('features.4.block.2.fc2.weight', tensor([[[[ 0.5486]],\n",
      "\n",
      "         [[-0.1382]],\n",
      "\n",
      "         [[ 0.1253]],\n",
      "\n",
      "         ...,\n",
      "\n",
      "         [[ 0.0605]],\n",
      "\n",
      "         [[-0.3067]],\n",
      "\n",
      "         [[ 0.1598]]],\n",
      "\n",
      "\n",
      "        [[[-0.0792]],\n",
      "\n",
      "         [[-0.1822]],\n",
      "\n",
      "         [[ 0.5030]],\n",
      "\n",
      "         ...,\n",
      "\n",
      "         [[ 0.0753]],\n",
      "\n",
      "         [[ 0.0594]],\n",
      "\n",
      "         [[-0.2495]]],\n",
      "\n",
      "\n",
      "        [[[ 0.0324]],\n",
      "\n",
      "         [[-0.0442]],\n",
      "\n",
      "         [[-0.0383]],\n",
      "\n",
      "         ...,\n",
      "\n",
      "         [[-0.2004]],\n",
      "\n",
      "         [[ 0.3212]],\n",
      "\n",
      "         [[-0.1385]]],\n",
      "\n",
      "\n",
      "        ...,\n",
      "\n",
      "\n",
      "        [[[-0.2656]],\n",
      "\n",
      "         [[ 0.5189]],\n",
      "\n",
      "         [[ 0.0654]],\n",
      "\n",
      "         ...,\n",
      "\n",
      "         [[-0.0981]],\n",
      "\n",
      "         [[ 0.1634]],\n",
      "\n",
      "         [[ 0.1512]]],\n",
      "\n",
      "\n",
      "        [[[ 0.1909]],\n",
      "\n",
      "         [[ 0.1909]],\n",
      "\n",
      "         [[-0.1756]],\n",
      "\n",
      "         ...,\n",
      "\n",
      "         [[-0.0916]],\n",
      "\n",
      "         [[ 0.0458]],\n",
      "\n",
      "         [[ 0.0878]]],\n",
      "\n",
      "\n",
      "        [[[-0.1006]],\n",
      "\n",
      "         [[ 0.1222]],\n",
      "\n",
      "         [[-0.0934]],\n",
      "\n",
      "         ...,\n",
      "\n",
      "         [[ 0.3593]],\n",
      "\n",
      "         [[ 0.0898]],\n",
      "\n",
      "         [[-0.0180]]]], size=(72, 24, 1, 1), dtype=torch.qint8,\n",
      "       quantization_scheme=torch.per_channel_affine,\n",
      "       scale=tensor([0.0043, 0.0040, 0.0029, 0.0038, 0.0032, 0.0040, 0.0026, 0.0044, 0.0030,\n",
      "        0.0027, 0.0037, 0.0027, 0.0032, 0.0023, 0.0028, 0.0029, 0.0029, 0.0024,\n",
      "        0.0022, 0.0029, 0.0027, 0.0028, 0.0029, 0.0030, 0.0032, 0.0048, 0.0026,\n",
      "        0.0040, 0.0029, 0.0039, 0.0034, 0.0038, 0.0022, 0.0033, 0.0036, 0.0026,\n",
      "        0.0032, 0.0024, 0.0027, 0.0028, 0.0030, 0.0026, 0.0024, 0.0031, 0.0024,\n",
      "        0.0029, 0.0027, 0.0028, 0.0027, 0.0040, 0.0041, 0.0029, 0.0043, 0.0030,\n",
      "        0.0042, 0.0021, 0.0025, 0.0025, 0.0035, 0.0027, 0.0032, 0.0030, 0.0029,\n",
      "        0.0036, 0.0032, 0.0028, 0.0033, 0.0023, 0.0032, 0.0041, 0.0038, 0.0036],\n",
      "       dtype=torch.float64),\n",
      "       zero_point=tensor([0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0,\n",
      "        0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0,\n",
      "        0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0]),\n",
      "       axis=0)), ('features.4.block.2.fc2.bias', Parameter containing:\n",
      "tensor([-0.0180,  0.0083,  0.0337,  0.0252, -0.0029, -0.0802, -0.0385, -0.0132,\n",
      "         0.0653,  0.0262,  0.0624, -0.0304,  0.0053, -0.0258, -0.0030,  0.0502,\n",
      "         0.0414, -0.0379,  0.0019, -0.1073, -0.0004,  0.0148,  0.0122,  0.0267,\n",
      "         0.0390, -0.0099, -0.0013, -0.0029,  0.0328,  0.0185,  0.0060, -0.0222,\n",
      "        -0.0081,  0.0340,  0.0268, -0.0337, -0.0215,  0.0134, -0.0480,  0.0353,\n",
      "         0.0165, -0.0066,  0.0201,  0.0562, -0.0398, -0.0200,  0.0163, -0.0480,\n",
      "         0.0069,  0.0231, -0.0066,  0.0416,  0.0310,  0.0474, -0.0078,  0.0414,\n",
      "         0.0181, -0.0355, -0.0058,  0.0170, -0.0275, -0.0031,  0.0320, -0.0460,\n",
      "        -0.0103, -0.0058,  0.0094, -0.0009, -0.0196, -0.0075, -0.0556, -0.0117],\n",
      "       requires_grad=True)), ('features.4.block.2.fc2.scale', tensor(0.0270)), ('features.4.block.2.fc2.zero_point', tensor(70)), ('features.4.block.2.skip_mul.scale', tensor(0.0533)), ('features.4.block.2.skip_mul.zero_point', tensor(0)), ('features.4.block.3.0.weight', tensor([[[[-0.3787]],\n",
      "\n",
      "         [[ 0.0105]],\n",
      "\n",
      "         [[ 0.3577]],\n",
      "\n",
      "         ...,\n",
      "\n",
      "         [[ 0.0842]],\n",
      "\n",
      "         [[ 0.2525]],\n",
      "\n",
      "         [[ 0.2735]]],\n",
      "\n",
      "\n",
      "        [[[ 0.1016]],\n",
      "\n",
      "         [[-0.1863]],\n",
      "\n",
      "         [[ 0.0000]],\n",
      "\n",
      "         ...,\n",
      "\n",
      "         [[-0.2032]],\n",
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      "         [[ 0.4233]],\n",
      "\n",
      "         [[-0.5503]]],\n",
      "\n",
      "\n",
      "        [[[ 0.0251]],\n",
      "\n",
      "         [[-0.2443]],\n",
      "\n",
      "         [[-0.3508]],\n",
      "\n",
      "         ...,\n",
      "\n",
      "         [[-0.6703]],\n",
      "\n",
      "         [[-0.0689]],\n",
      "\n",
      "         [[-0.4448]]],\n",
      "\n",
      "\n",
      "        ...,\n",
      "\n",
      "\n",
      "        [[[ 0.3247]],\n",
      "\n",
      "         [[ 0.2131]],\n",
      "\n",
      "         [[ 0.1420]],\n",
      "\n",
      "         ...,\n",
      "\n",
      "         [[ 0.0101]],\n",
      "\n",
      "         [[ 0.0913]],\n",
      "\n",
      "         [[-0.1319]]],\n",
      "\n",
      "\n",
      "        [[[ 0.6372]],\n",
      "\n",
      "         [[-0.0540]],\n",
      "\n",
      "         [[-0.1512]],\n",
      "\n",
      "         ...,\n",
      "\n",
      "         [[-0.7560]],\n",
      "\n",
      "         [[-0.8208]],\n",
      "\n",
      "         [[ 0.0108]]],\n",
      "\n",
      "\n",
      "        [[[ 0.1801]],\n",
      "\n",
      "         [[-0.2556]],\n",
      "\n",
      "         [[ 0.3427]],\n",
      "\n",
      "         ...,\n",
      "\n",
      "         [[-0.5577]],\n",
      "\n",
      "         [[-0.4822]],\n",
      "\n",
      "         [[-0.0174]]]], size=(40, 72, 1, 1), dtype=torch.qint8,\n",
      "       quantization_scheme=torch.per_channel_affine,\n",
      "       scale=tensor([0.0105, 0.0085, 0.0063, 0.0076, 0.0058, 0.0080, 0.0059, 0.0070, 0.0087,\n",
      "        0.0103, 0.0078, 0.0105, 0.0082, 0.0126, 0.0071, 0.0073, 0.0066, 0.0082,\n",
      "        0.0091, 0.0083, 0.0059, 0.0090, 0.0057, 0.0102, 0.0081, 0.0054, 0.0055,\n",
      "        0.0071, 0.0075, 0.0084, 0.0076, 0.0090, 0.0080, 0.0077, 0.0075, 0.0064,\n",
      "        0.0111, 0.0101, 0.0108, 0.0058], dtype=torch.float64),\n",
      "       zero_point=tensor([0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0,\n",
      "        0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0]),\n",
      "       axis=0)), ('features.4.block.3.0.bias', Parameter containing:\n",
      "tensor([ 7.2161e-01,  6.3651e-01,  3.7167e-01,  2.7853e-01,  6.8591e-01,\n",
      "         3.3035e-01,  1.1317e-01, -1.2761e-01, -4.7194e-01, -8.7008e-01,\n",
      "         4.2209e-01,  2.4891e-01,  3.0530e-01, -9.5520e-02,  3.4839e-04,\n",
      "         7.1771e-01, -8.7648e-01,  1.4760e-01, -3.7463e-01, -4.1726e-01,\n",
      "         2.5529e-01, -2.7950e-01, -2.7993e-01, -1.1249e-01, -1.5062e+00,\n",
      "         1.1021e+00,  5.0460e-01, -1.4339e-01,  1.5624e-01,  2.1760e-01,\n",
      "        -2.0967e-01,  5.2701e-01,  2.8126e-02, -1.8777e-01,  3.1339e-01,\n",
      "        -3.8368e-01, -3.0348e-01, -7.2336e-01, -1.6757e-02,  5.2896e-01])), ('features.4.block.3.0.scale', tensor(0.1358)), ('features.4.block.3.0.zero_point', tensor(62)), ('features.4.skip_add.scale', tensor(1.)), ('features.4.skip_add.zero_point', tensor(0)), ('features.5.block.0.0.weight', tensor([[[[ 0.1087]],\n",
      "\n",
      "         [[ 0.2207]],\n",
      "\n",
      "         [[ 0.1153]],\n",
      "\n",
      "         ...,\n",
      "\n",
      "         [[-0.4216]],\n",
      "\n",
      "         [[ 0.0231]],\n",
      "\n",
      "         [[ 0.1878]]],\n",
      "\n",
      "\n",
      "        [[[-0.2399]],\n",
      "\n",
      "         [[-0.0284]],\n",
      "\n",
      "         [[-0.1484]],\n",
      "\n",
      "         ...,\n",
      "\n",
      "         [[-0.0568]],\n",
      "\n",
      "         [[-0.1610]],\n",
      "\n",
      "         [[-0.0189]]],\n",
      "\n",
      "\n",
      "        [[[-0.0987]],\n",
      "\n",
      "         [[ 0.2176]],\n",
      "\n",
      "         [[ 0.0987]],\n",
      "\n",
      "         ...,\n",
      "\n",
      "         [[-0.0958]],\n",
      "\n",
      "         [[-0.0058]],\n",
      "\n",
      "         [[-0.0435]]],\n",
      "\n",
      "\n",
      "        ...,\n",
      "\n",
      "\n",
      "        [[[ 0.0426]],\n",
      "\n",
      "         [[-0.0269]],\n",
      "\n",
      "         [[ 0.0224]],\n",
      "\n",
      "         ...,\n",
      "\n",
      "         [[-0.0448]],\n",
      "\n",
      "         [[-0.0538]],\n",
      "\n",
      "         [[ 0.0022]]],\n",
      "\n",
      "\n",
      "        [[[ 0.0728]],\n",
      "\n",
      "         [[ 0.1769]],\n",
      "\n",
      "         [[-0.0853]],\n",
      "\n",
      "         ...,\n",
      "\n",
      "         [[-0.0499]],\n",
      "\n",
      "         [[ 0.1561]],\n",
      "\n",
      "         [[-0.0749]]],\n",
      "\n",
      "\n",
      "        [[[-0.0416]],\n",
      "\n",
      "         [[ 0.0243]],\n",
      "\n",
      "         [[-0.0208]],\n",
      "\n",
      "         ...,\n",
      "\n",
      "         [[-0.0971]],\n",
      "\n",
      "         [[-0.3018]],\n",
      "\n",
      "         [[-0.1006]]]], size=(120, 40, 1, 1), dtype=torch.qint8,\n",
      "       quantization_scheme=torch.per_channel_affine,\n",
      "       scale=tensor([0.0033, 0.0032, 0.0029, 0.0020, 0.0057, 0.0028, 0.0028, 0.0043, 0.0032,\n",
      "        0.0037, 0.0024, 0.0026, 0.0025, 0.0032, 0.0025, 0.0034, 0.0021, 0.0030,\n",
      "        0.0017, 0.0024, 0.0024, 0.0026, 0.0023, 0.0035, 0.0033, 0.0028, 0.0041,\n",
      "        0.0026, 0.0034, 0.0034, 0.0032, 0.0023, 0.0033, 0.0022, 0.0027, 0.0022,\n",
      "        0.0040, 0.0021, 0.0027, 0.0031, 0.0033, 0.0031, 0.0044, 0.0031, 0.0031,\n",
      "        0.0027, 0.0037, 0.0026, 0.0036, 0.0027, 0.0039, 0.0028, 0.0021, 0.0023,\n",
      "        0.0039, 0.0023, 0.0025, 0.0031, 0.0043, 0.0023, 0.0031, 0.0030, 0.0033,\n",
      "        0.0019, 0.0029, 0.0026, 0.0031, 0.0036, 0.0030, 0.0023, 0.0022, 0.0029,\n",
      "        0.0024, 0.0050, 0.0026, 0.0032, 0.0020, 0.0031, 0.0031, 0.0023, 0.0033,\n",
      "        0.0023, 0.0021, 0.0037, 0.0028, 0.0036, 0.0028, 0.0046, 0.0023, 0.0026,\n",
      "        0.0027, 0.0028, 0.0027, 0.0032, 0.0030, 0.0029, 0.0023, 0.0031, 0.0037,\n",
      "        0.0023, 0.0040, 0.0045, 0.0025, 0.0031, 0.0045, 0.0043, 0.0037, 0.0039,\n",
      "        0.0038, 0.0028, 0.0030, 0.0018, 0.0033, 0.0033, 0.0034, 0.0027, 0.0024,\n",
      "        0.0022, 0.0021, 0.0035], dtype=torch.float64),\n",
      "       zero_point=tensor([0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0,\n",
      "        0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0,\n",
      "        0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0,\n",
      "        0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0,\n",
      "        0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0]),\n",
      "       axis=0)), ('features.5.block.0.0.bias', Parameter containing:\n",
      "tensor([ 0.0586, -0.0540,  0.0013, -0.0002,  0.0713, -0.0559, -0.0181, -0.0232,\n",
      "         0.0089,  0.0093,  0.0219,  0.0302,  0.0116, -0.0275,  0.0636, -0.0160,\n",
      "        -0.0098,  0.0076,  0.0246, -0.0244, -0.0852,  0.0043,  0.0568,  0.0179,\n",
      "         0.1389,  0.0670,  0.0340,  0.0655, -0.0237,  0.0886,  0.0722, -0.0034,\n",
      "         0.0103, -0.0312, -0.0180, -0.0368,  0.0641, -0.0738,  0.0125,  0.0594,\n",
      "         0.0408,  0.0679,  0.1617,  0.1308,  0.0688,  0.0136, -0.0386,  0.0250,\n",
      "         0.0210,  0.0562,  0.1182,  0.1039, -0.0794, -0.0488, -0.0091,  0.0027,\n",
      "        -0.0102, -0.0272,  0.0256,  0.0876,  0.0114, -0.0676,  0.0089,  0.0906,\n",
      "        -0.0158, -0.0643,  0.0192,  0.0597, -0.0577,  0.0215,  0.0156,  0.0560,\n",
      "         0.0568,  0.1265,  0.0121, -0.0617, -0.0240, -0.1368, -0.0932, -0.0394,\n",
      "         0.0619, -0.0197, -0.1020,  0.0020, -0.0415,  0.0460,  0.0247,  0.1134,\n",
      "        -0.0050,  0.0384, -0.0597, -0.0084,  0.0393, -0.0790, -0.0236, -0.0456,\n",
      "         0.0144, -0.0332, -0.0483,  0.0337, -0.0206, -0.0400,  0.0527,  0.0522,\n",
      "        -0.0968, -0.0284, -0.0102, -0.0241,  0.0724,  0.0184,  0.0076, -0.0164,\n",
      "         0.0418,  0.0442,  0.0380,  0.0501, -0.0149, -0.0375, -0.0706, -0.0343])), ('features.5.block.0.0.scale', tensor(0.0771)), ('features.5.block.0.0.zero_point', tensor(0)), ('features.5.block.1.0.weight', tensor([[[[-0.4702, -0.1212, -0.1696, -0.1066,  0.1357],\n",
      "          [-0.2230, -0.1260, -0.3441, -0.1454, -0.1648],\n",
      "          [ 0.0194,  0.0242,  0.2036, -0.1551,  0.2957],\n",
      "          [ 0.0388, -0.6204, -0.0048,  0.0824,  0.1406],\n",
      "          [ 0.0630,  0.1600,  0.4944,  0.1745,  0.5865]]],\n",
      "\n",
      "\n",
      "        [[[ 0.0762,  0.1524, -0.7342,  0.0554,  0.0000],\n",
      "          [-0.0208,  0.0554, -0.3186, -0.3810, -0.0623],\n",
      "          [-0.2078, -0.0762,  0.0485, -0.2771, -0.2424],\n",
      "          [-0.0762, -0.1316, -0.0762,  0.0208, -0.0693],\n",
      "          [ 0.0000, -0.0208, -0.0831, -0.4502, -0.3186]]],\n",
      "\n",
      "\n",
      "        [[[ 0.4065,  0.1919,  0.0301, -0.0113,  0.0903],\n",
      "          [ 0.2108, -0.4065, -0.3613, -0.2898, -0.1355],\n",
      "          [ 0.3124, -0.0339,  0.0753, -0.4817,  0.0226],\n",
      "          [ 0.3651, -0.0903,  0.0188, -0.3651, -0.3914],\n",
      "          [ 0.2710,  0.0527,  0.1242,  0.2484, -0.0753]]],\n",
      "\n",
      "\n",
      "        ...,\n",
      "\n",
      "\n",
      "        [[[ 0.2559,  0.3199,  0.2102,  0.1828,  0.1280],\n",
      "          [-0.2742,  0.1736,  0.3382,  0.2285,  0.2285],\n",
      "          [-0.0366, -0.0183,  0.5849,  0.3290, -0.0274],\n",
      "          [ 0.1371, -0.1736, -0.0274,  0.1828, -0.0823],\n",
      "          [ 1.1607,  0.0823, -0.0914,  0.6489, -0.1462]]],\n",
      "\n",
      "\n",
      "        [[[ 0.9403, -0.2813,  0.2073,  0.2369,  0.2591],\n",
      "          [-0.0740,  0.0074,  0.3184, -0.0370,  0.0074],\n",
      "          [-0.3628, -0.5775, -0.6145, -0.3850, -0.2813],\n",
      "          [-0.1555, -0.3406, -0.4516, -0.0888, -0.2739],\n",
      "          [-0.1777, -0.5257,  0.1777,  0.2665,  0.6737]]],\n",
      "\n",
      "\n",
      "        [[[-0.4018, -0.1848, -0.0562, -0.0804, -0.0884],\n",
      "          [ 0.0964,  0.5946, -0.0643,  0.2812,  0.3616],\n",
      "          [ 0.2491, -0.1205,  0.1286,  0.1848,  0.1928],\n",
      "          [-0.1768,  0.0161, -1.0285, -0.1687, -0.3295],\n",
      "          [ 0.0080, -0.0723, -0.1687,  0.0643,  0.2571]]]],\n",
      "       size=(120, 1, 5, 5), dtype=torch.qint8,\n",
      "       quantization_scheme=torch.per_channel_affine,\n",
      "       scale=tensor([0.0048, 0.0069, 0.0038, 0.0075, 0.0132, 0.0088, 0.0085, 0.0042, 0.0073,\n",
      "        0.0080, 0.0039, 0.0046, 0.0062, 0.0024, 0.0051, 0.0032, 0.0056, 0.0036,\n",
      "        0.0083, 0.0035, 0.0104, 0.0056, 0.0023, 0.0061, 0.0088, 0.0047, 0.0041,\n",
      "        0.0065, 0.0038, 0.0061, 0.0051, 0.0036, 0.0056, 0.0057, 0.0015, 0.0038,\n",
      "        0.0071, 0.0061, 0.0079, 0.0058, 0.0045, 0.0062, 0.0037, 0.0055, 0.0035,\n",
      "        0.0074, 0.0096, 0.0070, 0.0066, 0.0056, 0.0089, 0.0067, 0.0042, 0.0071,\n",
      "        0.0036, 0.0029, 0.0034, 0.0060, 0.0081, 0.0060, 0.0051, 0.0072, 0.0065,\n",
      "        0.0035, 0.0073, 0.0039, 0.0034, 0.0054, 0.0052, 0.0107, 0.0039, 0.0038,\n",
      "        0.0033, 0.0069, 0.0069, 0.0059, 0.0028, 0.0045, 0.0112, 0.0053, 0.0054,\n",
      "        0.0059, 0.0071, 0.0053, 0.0037, 0.0079, 0.0079, 0.0048, 0.0066, 0.0077,\n",
      "        0.0052, 0.0075, 0.0051, 0.0064, 0.0072, 0.0056, 0.0023, 0.0067, 0.0049,\n",
      "        0.0062, 0.0043, 0.0041, 0.0058, 0.0048, 0.0071, 0.0036, 0.0028, 0.0072,\n",
      "        0.0030, 0.0063, 0.0035, 0.0079, 0.0052, 0.0060, 0.0033, 0.0051, 0.0063,\n",
      "        0.0091, 0.0074, 0.0080], dtype=torch.float64),\n",
      "       zero_point=tensor([0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0,\n",
      "        0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0,\n",
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      "       axis=0)), ('features.5.block.1.0.bias', Parameter containing:\n",
      "tensor([ 0.0681,  1.0839,  0.0432, -0.4217, -0.0590,  0.0532, -0.6737,  0.9220,\n",
      "         0.8955,  0.0249,  0.6613, -0.5080, -0.5715,  0.5879,  1.1487, -0.8684,\n",
      "         0.2195, -0.6732,  0.5579, -0.5057,  0.6870,  1.5108, -0.6107, -0.5565,\n",
      "         0.4307,  0.5055,  0.6108, -1.3428, -0.1296,  1.1058,  1.4932,  0.7423,\n",
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      "         0.0418,  0.9121,  0.8036,  0.8297,  0.7805,  0.5519, -0.6398, -0.0963,\n",
      "         0.7658,  0.1735,  1.0161,  0.3147, -1.2716, -0.2030, -0.1829, -0.6489,\n",
      "        -0.2121,  0.7949,  0.8580,  0.9787, -0.5339,  0.6524,  1.1555,  0.4932,\n",
      "        -0.6087,  0.3316, -0.3717, -0.7645, -0.7707,  0.5012,  0.2821,  0.4561,\n",
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      "        [[[-0.4847]],\n",
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      "        [[[-0.3633]],\n",
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      "         [[ 0.1069]],\n",
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      "         [[-0.2607]],\n",
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      "         ...,\n",
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      "         [[ 0.1582]],\n",
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      "         [[-0.4360]],\n",
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      "         [[-0.0598]]]], size=(32, 120, 1, 1), dtype=torch.qint8,\n",
      "       quantization_scheme=torch.per_channel_affine,\n",
      "       scale=tensor([0.0050, 0.0063, 0.0049, 0.0055, 0.0067, 0.0070, 0.0063, 0.0052, 0.0068,\n",
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      "       zero_point=tensor([0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0,\n",
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      "       axis=0)), ('features.5.block.2.fc1.bias', Parameter containing:\n",
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      "       requires_grad=True)), ('features.5.block.2.fc1.scale', tensor(0.0529)), ('features.5.block.2.fc1.zero_point', tensor(0)), ('features.5.block.2.fc2.weight', tensor([[[[ 0.3531]],\n",
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      "         [[ 0.1919]],\n",
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      "       quantization_scheme=torch.per_channel_affine,\n",
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      "        0.0014, 0.0024, 0.0035, 0.0034, 0.0023, 0.0029, 0.0022, 0.0025, 0.0027,\n",
      "        0.0025, 0.0029, 0.0023, 0.0024, 0.0021, 0.0023, 0.0034, 0.0020, 0.0022,\n",
      "        0.0035, 0.0031, 0.0034], dtype=torch.float64),\n",
      "       zero_point=tensor([0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0,\n",
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      "       axis=0)), ('features.5.block.2.fc2.bias', Parameter containing:\n",
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      "         0.0256,  0.0243,  0.0335,  0.0019, -0.0518,  0.0143,  0.0239,  0.0195,\n",
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      "        [[[ 0.2589]],\n",
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      "         [[ 0.4242]],\n",
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      "         ...,\n",
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      "         [[-0.0992]],\n",
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      "\n",
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      "         ...,\n",
      "\n",
      "         [[ 0.0317]],\n",
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      "         [[-0.2411]],\n",
      "\n",
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      "       quantization_scheme=torch.per_channel_affine,\n",
      "       scale=tensor([0.0063, 0.0051, 0.0060, 0.0049, 0.0066, 0.0055, 0.0047, 0.0058, 0.0058,\n",
      "        0.0052, 0.0046, 0.0040, 0.0050, 0.0054, 0.0067, 0.0072, 0.0050, 0.0073,\n",
      "        0.0049, 0.0074, 0.0037, 0.0045, 0.0057, 0.0063, 0.0063, 0.0051, 0.0095,\n",
      "        0.0065, 0.0048, 0.0046, 0.0055, 0.0071, 0.0051, 0.0092, 0.0045, 0.0073,\n",
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      "       zero_point=tensor([0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0,\n",
      "        0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0]),\n",
      "       axis=0)), ('features.5.block.3.0.bias', Parameter containing:\n",
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      "        -0.8250, -0.6796,  0.0942, -1.0055,  0.2910,  0.1306, -0.6923, -0.5340,\n",
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      "        -0.3787, -0.0872,  0.6639,  0.1582,  0.2080, -0.1660,  0.4424,  0.3803,\n",
      "        -0.5099, -1.0095,  0.2196, -0.1635, -0.0704,  0.6143, -0.9101,  0.4746])), ('features.5.block.3.0.scale', tensor(0.1462)), ('features.5.block.3.0.zero_point', tensor(67)), ('features.5.skip_add.scale', tensor(0.1996)), ('features.5.skip_add.zero_point', tensor(62)), ('features.6.block.0.0.weight', tensor([[[[-0.2237]],\n",
      "\n",
      "         [[ 0.0518]],\n",
      "\n",
      "         [[ 0.0018]],\n",
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      "         ...,\n",
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      "         [[-0.0703]],\n",
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      "         [[-0.1165]],\n",
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      "\n",
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      "        [[[-0.0264]],\n",
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      "         [[-0.0446]],\n",
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      "\n",
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      "        [[[-0.0116]],\n",
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      "         [[ 0.0328]],\n",
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      "       quantization_scheme=torch.per_channel_affine,\n",
      "       scale=tensor([0.0018, 0.0018, 0.0017, 0.0025, 0.0029, 0.0019, 0.0024, 0.0016, 0.0010,\n",
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      "        0.0012, 0.0025, 0.0025, 0.0020, 0.0022, 0.0024, 0.0022, 0.0018, 0.0021,\n",
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      "        0.0024, 0.0017, 0.0015, 0.0019, 0.0017, 0.0014, 0.0027, 0.0021, 0.0024,\n",
      "        0.0023, 0.0016, 0.0027, 0.0020, 0.0021, 0.0020, 0.0019, 0.0016, 0.0020,\n",
      "        0.0015, 0.0021, 0.0022, 0.0013, 0.0015, 0.0022, 0.0028, 0.0025, 0.0031,\n",
      "        0.0022, 0.0020, 0.0016, 0.0024, 0.0018, 0.0019, 0.0016, 0.0016, 0.0021,\n",
      "        0.0012, 0.0017, 0.0019], dtype=torch.float64),\n",
      "       zero_point=tensor([0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0,\n",
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      "       axis=0)), ('features.6.block.0.0.bias', Parameter containing:\n",
      "tensor([-1.7260e-02, -3.9907e-03,  1.7985e-02, -5.9382e-02, -4.9196e-02,\n",
      "         3.6429e-02, -1.0295e-02,  4.7604e-02, -1.5004e-02,  5.8979e-02,\n",
      "         7.3412e-04, -2.4488e-03,  8.5118e-02,  4.6780e-02,  5.8312e-02,\n",
      "        -3.5424e-02, -5.3206e-03, -7.8074e-02, -4.0448e-02, -3.0027e-02,\n",
      "         3.6201e-02,  5.4114e-02,  3.3920e-02, -5.5871e-02, -2.0928e-03,\n",
      "        -4.7397e-02,  6.6460e-02, -3.9630e-02,  1.9680e-02,  8.4227e-02,\n",
      "        -7.8544e-02, -4.9466e-02,  2.2817e-03, -4.7272e-02,  4.0229e-02,\n",
      "         2.8557e-02,  1.7814e-03,  1.6042e-01, -1.0994e-01, -2.8885e-02,\n",
      "         1.1587e-01, -2.5340e-02,  3.6003e-02, -2.6104e-02, -4.1468e-03,\n",
      "        -2.9849e-02, -4.0698e-02,  5.6227e-02,  9.2037e-02, -2.1297e-02,\n",
      "        -5.4153e-02,  2.8254e-02, -1.6131e-02,  6.0866e-02,  1.1707e-04,\n",
      "         9.2349e-02, -3.2707e-02,  3.8711e-02,  6.1859e-02,  3.4409e-02,\n",
      "         5.4062e-02,  5.2202e-02, -3.0855e-02,  7.4422e-03, -3.7860e-03,\n",
      "        -4.3081e-02, -1.7855e-03, -3.8067e-03, -7.3861e-02, -3.4151e-03,\n",
      "        -2.2270e-02, -1.1201e-01,  5.0547e-03,  8.4931e-03, -2.2516e-02,\n",
      "        -1.3382e-01,  2.5241e-02,  7.8464e-04,  1.1234e-01,  2.3390e-03,\n",
      "         3.7263e-02, -1.6387e-01, -3.5704e-02,  3.2058e-02,  9.5883e-03,\n",
      "        -2.6487e-02,  1.1128e-02, -2.9115e-02, -9.4825e-02,  7.4241e-02,\n",
      "        -5.3991e-03,  9.9297e-02,  1.9846e-02, -2.7542e-02, -4.6047e-02,\n",
      "         2.7483e-02, -6.7561e-02,  2.7236e-03, -4.2966e-02,  4.8715e-02,\n",
      "         1.6118e-02, -1.5424e-01,  3.1492e-03, -3.8906e-03,  4.5532e-02,\n",
      "         3.0309e-02,  6.0282e-02,  2.1062e-02, -1.0266e-01, -2.4134e-02,\n",
      "         4.4156e-02,  2.5597e-02,  2.3731e-02, -9.5148e-02, -1.8267e-02,\n",
      "        -8.1345e-02, -3.7581e-02, -1.6952e-02, -4.5030e-03, -6.2913e-02])), ('features.6.block.0.0.scale', tensor(0.0791)), ('features.6.block.0.0.zero_point', tensor(0)), ('features.6.block.1.0.weight', tensor([[[[ 0.7012, -0.5167, -0.0923,  0.3783,  0.1015],\n",
      "          [ 0.4152,  0.6643,  0.3414,  0.2030,  0.6366],\n",
      "          [-0.7012, -0.2122, -0.3875, -0.1199, -0.0830],\n",
      "          [ 0.1384,  1.1718,  0.5075, -0.0923, -0.0923],\n",
      "          [-0.3967, -0.5444, -0.4429,  0.1938, -0.3875]]],\n",
      "\n",
      "\n",
      "        [[[ 0.0567,  0.1083,  0.3043, -0.0567, -0.2166],\n",
      "          [-0.0103,  0.1547,  0.1289,  0.2166,  0.1186],\n",
      "          [ 0.0516,  0.1392,  0.6137,  0.4332,  0.1650],\n",
      "          [ 0.0000,  0.1135,  0.0825,  0.1805,  0.1444],\n",
      "          [-0.6291, -0.1599,  0.5312,  0.2166,  0.1031]]],\n",
      "\n",
      "\n",
      "        [[[ 0.2331,  0.3199, -0.1843,  0.4446, -0.1193],\n",
      "          [ 0.6885,  0.4500,  0.2114,  0.3578,  0.0705],\n",
      "          [-0.1735, -0.0705,  0.0705,  0.3524,  0.0813],\n",
      "          [ 0.1681, -0.0380, -0.0542, -0.1464, -0.1464],\n",
      "          [ 0.1681, -0.2494, -0.4717, -0.4662, -0.3849]]],\n",
      "\n",
      "\n",
      "        ...,\n",
      "\n",
      "\n",
      "        [[[-0.0185, -0.1018, -0.2035, -0.2405, -0.3793],\n",
      "          [-0.3839,  0.1341,  0.0694, -0.0601, -0.1110],\n",
      "          [-0.1018, -0.0416, -0.3423, -0.1018, -0.5921],\n",
      "          [-0.2637,  0.1573, -0.2914, -0.1341, -0.4071],\n",
      "          [-0.1619, -0.1018,  0.4764, -0.2128,  0.0694]]],\n",
      "\n",
      "\n",
      "        [[[-0.2179,  0.0947,  0.0284,  1.2033, -0.7485],\n",
      "          [-0.1232,  0.0000,  0.1326,  0.4169, -0.2179],\n",
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      "          [-0.2937,  0.0379,  1.0138, -1.2127,  0.0000]]],\n",
      "\n",
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      "          [-0.0216,  0.0504,  0.0793, -0.0504, -0.5837],\n",
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      "          [-0.1009, -0.0432,  0.0793,  0.0216, -0.8287]]]],\n",
      "       size=(120, 1, 5, 5), dtype=torch.qint8,\n",
      "       quantization_scheme=torch.per_channel_affine,\n",
      "       scale=tensor([0.0092, 0.0052, 0.0054, 0.0067, 0.0056, 0.0071, 0.0042, 0.0031, 0.0062,\n",
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      "        0.0086, 0.0051, 0.0036, 0.0025, 0.0069, 0.0090, 0.0038, 0.0041, 0.0057,\n",
      "        0.0042, 0.0028, 0.0086, 0.0035, 0.0069, 0.0053, 0.0040, 0.0055, 0.0071,\n",
      "        0.0065, 0.0065, 0.0056, 0.0039, 0.0061, 0.0048, 0.0036, 0.0052, 0.0068,\n",
      "        0.0039, 0.0069, 0.0083, 0.0048, 0.0078, 0.0049, 0.0072, 0.0043, 0.0052,\n",
      "        0.0042, 0.0106, 0.0038, 0.0056, 0.0061, 0.0040, 0.0094, 0.0075, 0.0062,\n",
      "        0.0057, 0.0063, 0.0038, 0.0027, 0.0094, 0.0039, 0.0030, 0.0040, 0.0077,\n",
      "        0.0026, 0.0036, 0.0038, 0.0035, 0.0046, 0.0064, 0.0038, 0.0058, 0.0041,\n",
      "        0.0078, 0.0048, 0.0052, 0.0058, 0.0067, 0.0040, 0.0076, 0.0058, 0.0070,\n",
      "        0.0046, 0.0095, 0.0072], dtype=torch.float64),\n",
      "       zero_point=tensor([0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0,\n",
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      "       axis=0)), ('features.6.block.1.0.bias', Parameter containing:\n",
      "tensor([-4.7813e-01, -9.5781e-01, -2.7208e-01,  4.0735e-01, -9.2824e-01,\n",
      "         2.0345e-01,  1.0363e+00, -1.2153e+00, -1.1336e+00, -8.7477e-01,\n",
      "         1.3125e+00, -6.2671e-01,  5.0588e-01, -1.0183e+00,  3.3800e-01,\n",
      "        -8.6952e-01,  8.6882e-02,  9.7521e-01, -1.3582e+00,  8.3860e-01,\n",
      "        -3.9041e-01, -7.4166e-01, -6.8966e-01, -4.1907e-01, -6.8295e-01,\n",
      "         2.3825e-02, -7.7978e-01, -1.1926e+00, -8.2383e-01, -3.8027e-01,\n",
      "        -8.5740e-01, -9.2457e-02,  5.6587e-01, -8.4076e-01,  8.4434e-01,\n",
      "        -7.0452e-01, -7.3951e-01,  8.2335e-01, -4.4706e-01,  6.7035e-01,\n",
      "         5.2896e-01,  7.3820e-01,  1.1424e+00, -7.3028e-01, -8.1061e-01,\n",
      "        -5.5487e-01,  8.2979e-01,  6.4103e-01,  8.9695e-01,  1.1350e-02,\n",
      "         7.9879e-01,  1.4161e+00,  4.7335e-01,  1.1477e+00,  7.5174e-01,\n",
      "        -6.7827e-01,  9.3840e-02,  9.4086e-01, -1.6377e-01,  1.1670e+00,\n",
      "         1.4711e+00,  1.1228e+00,  6.0516e-01, -1.3983e-03, -6.3010e-01,\n",
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      "        -8.5842e-02,  1.3284e+00, -1.5551e-01,  1.2627e+00, -5.1108e-01,\n",
      "        -1.3075e-01, -4.8102e-01,  8.1946e-01, -1.6559e-02,  1.5338e+00,\n",
      "         3.8588e-02,  6.5708e-01, -1.1162e+00,  1.0793e+00,  7.1204e-02,\n",
      "        -8.6689e-01, -8.5670e-01, -6.1268e-01, -3.3406e-01, -7.9178e-01,\n",
      "         9.0264e-01, -1.0942e+00,  5.1436e-01,  9.6997e-02,  2.3301e-02,\n",
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      "        -7.8539e-01,  1.2169e+00,  6.7308e-01,  8.5545e-01, -9.0444e-01,\n",
      "         1.3210e+00,  1.7008e-01,  1.2879e+00, -7.1675e-01,  1.1787e+00])), ('features.6.block.1.0.scale', tensor(0.1074)), ('features.6.block.1.0.zero_point', tensor(0)), ('features.6.block.2.fc1.weight', tensor([[[[-0.1911]],\n",
      "\n",
      "         [[ 0.2090]],\n",
      "\n",
      "         [[-0.3583]],\n",
      "\n",
      "         ...,\n",
      "\n",
      "         [[-0.2090]],\n",
      "\n",
      "         [[ 0.4956]],\n",
      "\n",
      "         [[-0.1254]]],\n",
      "\n",
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      "        [[[-0.6293]],\n",
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      "         [[-0.0363]],\n",
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      "         ...,\n",
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      "         [[ 0.0363]],\n",
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      "         [[ 0.2057]],\n",
      "\n",
      "         [[-0.3510]]],\n",
      "\n",
      "\n",
      "        [[[-0.1012]],\n",
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      "         [[-0.0844]],\n",
      "\n",
      "         [[-0.2362]],\n",
      "\n",
      "         ...,\n",
      "\n",
      "         [[ 0.0562]],\n",
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      "         [[-0.3318]],\n",
      "\n",
      "         [[-0.1406]]],\n",
      "\n",
      "\n",
      "        ...,\n",
      "\n",
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      "        [[[-0.3517]],\n",
      "\n",
      "         [[ 0.1005]],\n",
      "\n",
      "         [[-0.3567]],\n",
      "\n",
      "         ...,\n",
      "\n",
      "         [[-0.3919]],\n",
      "\n",
      "         [[-0.3366]],\n",
      "\n",
      "         [[-0.0251]]],\n",
      "\n",
      "\n",
      "        [[[-0.1215]],\n",
      "\n",
      "         [[ 0.0270]],\n",
      "\n",
      "         [[ 0.0585]],\n",
      "\n",
      "         ...,\n",
      "\n",
      "         [[-0.0315]],\n",
      "\n",
      "         [[-0.4678]],\n",
      "\n",
      "         [[ 0.4228]]],\n",
      "\n",
      "\n",
      "        [[[-0.2079]],\n",
      "\n",
      "         [[-0.4613]],\n",
      "\n",
      "         [[ 0.1876]],\n",
      "\n",
      "         ...,\n",
      "\n",
      "         [[ 0.2332]],\n",
      "\n",
      "         [[-0.2535]],\n",
      "\n",
      "         [[ 0.0963]]]], size=(32, 120, 1, 1), dtype=torch.qint8,\n",
      "       quantization_scheme=torch.per_channel_affine,\n",
      "       scale=tensor([0.0060, 0.0061, 0.0056, 0.0049, 0.0060, 0.0056, 0.0056, 0.0064, 0.0062,\n",
      "        0.0056, 0.0055, 0.0057, 0.0053, 0.0044, 0.0049, 0.0061, 0.0057, 0.0051,\n",
      "        0.0053, 0.0048, 0.0053, 0.0057, 0.0052, 0.0045, 0.0054, 0.0048, 0.0056,\n",
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      "       zero_point=tensor([0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0,\n",
      "        0, 0, 0, 0, 0, 0, 0, 0]),\n",
      "       axis=0)), ('features.6.block.2.fc1.bias', Parameter containing:\n",
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      "         0.0508,  0.0435, -0.0333, -0.0301, -0.0226, -0.0377, -0.0132, -0.0800,\n",
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      "       requires_grad=True)), ('features.6.block.2.fc1.scale', tensor(0.0402)), ('features.6.block.2.fc1.zero_point', tensor(0)), ('features.6.block.2.fc2.weight', tensor([[[[-0.0106]],\n",
      "\n",
      "         [[-0.0317]],\n",
      "\n",
      "         [[-0.0978]],\n",
      "\n",
      "         ...,\n",
      "\n",
      "         [[ 0.1031]],\n",
      "\n",
      "         [[-0.0291]],\n",
      "\n",
      "         [[ 0.0952]]],\n",
      "\n",
      "\n",
      "        [[[ 0.1330]],\n",
      "\n",
      "         [[-0.0222]],\n",
      "\n",
      "         [[ 0.1289]],\n",
      "\n",
      "         ...,\n",
      "\n",
      "         [[-0.0947]],\n",
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      "         [[-0.0544]],\n",
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      "         [[ 0.0081]]],\n",
      "\n",
      "\n",
      "        [[[-0.0784]],\n",
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      "         [[ 0.1268]],\n",
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      "         [[ 0.1521]],\n",
      "\n",
      "         ...,\n",
      "\n",
      "         [[ 0.1959]],\n",
      "\n",
      "         [[-0.0761]],\n",
      "\n",
      "         [[ 0.2167]]],\n",
      "\n",
      "\n",
      "        ...,\n",
      "\n",
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      "        [[[-0.1226]],\n",
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      "         [[ 0.1054]],\n",
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      "         ...,\n",
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      "         [[-0.1397]]],\n",
      "\n",
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      "        [[[ 0.1792]],\n",
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      "         [[ 0.0724]],\n",
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      "         [[ 0.0896]],\n",
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      "         [[-0.0327]],\n",
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      "         [[ 0.1482]]],\n",
      "\n",
      "\n",
      "        [[[ 0.0810]],\n",
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      "         [[-0.0405]],\n",
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      "         [[-0.0343]],\n",
      "\n",
      "         [[ 0.3457]],\n",
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      "         [[ 0.0311]]]], size=(120, 32, 1, 1), dtype=torch.qint8,\n",
      "       quantization_scheme=torch.per_channel_affine,\n",
      "       scale=tensor([0.0026, 0.0020, 0.0023, 0.0024, 0.0026, 0.0023, 0.0029, 0.0024, 0.0030,\n",
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      "        0.0020, 0.0027, 0.0020, 0.0022, 0.0027, 0.0028, 0.0024, 0.0035, 0.0031,\n",
      "        0.0027, 0.0024, 0.0027, 0.0029, 0.0030, 0.0019, 0.0029, 0.0019, 0.0022,\n",
      "        0.0031, 0.0029, 0.0028, 0.0031, 0.0026, 0.0021, 0.0025, 0.0036, 0.0029,\n",
      "        0.0023, 0.0023, 0.0032, 0.0022, 0.0022, 0.0033, 0.0019, 0.0026, 0.0026,\n",
      "        0.0029, 0.0023, 0.0020, 0.0031, 0.0020, 0.0025, 0.0020, 0.0029, 0.0028,\n",
      "        0.0028, 0.0023, 0.0022, 0.0025, 0.0021, 0.0015, 0.0026, 0.0028, 0.0024,\n",
      "        0.0032, 0.0026, 0.0028, 0.0034, 0.0029, 0.0029, 0.0026, 0.0028, 0.0029,\n",
      "        0.0025, 0.0017, 0.0031], dtype=torch.float64),\n",
      "       zero_point=tensor([0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0,\n",
      "        0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0,\n",
      "        0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0,\n",
      "        0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0,\n",
      "        0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0]),\n",
      "       axis=0)), ('features.6.block.2.fc2.bias', Parameter containing:\n",
      "tensor([ 0.0104, -0.0234, -0.0212, -0.0426,  0.0193,  0.0319,  0.0015,  0.0253,\n",
      "        -0.0402, -0.0426, -0.0334,  0.0007,  0.0240, -0.0248,  0.0018,  0.0683,\n",
      "         0.0230, -0.0217,  0.0387, -0.0295, -0.0252,  0.0339,  0.0149, -0.0670,\n",
      "        -0.0608,  0.0163, -0.0152,  0.0001, -0.0491, -0.0074,  0.0273, -0.0030,\n",
      "         0.0174, -0.0003,  0.0244, -0.1040, -0.0151, -0.0010,  0.0132, -0.0199,\n",
      "         0.0745, -0.0250,  0.0507,  0.0204, -0.0098,  0.0448,  0.0260, -0.0044,\n",
      "         0.0142,  0.0281, -0.0668,  0.0055, -0.0102,  0.0171, -0.0318,  0.0318,\n",
      "        -0.0333,  0.0329, -0.0367, -0.0128,  0.0600, -0.0089, -0.0030,  0.0254,\n",
      "         0.0135,  0.0217,  0.0340, -0.0270, -0.0126,  0.0116, -0.0289, -0.0213,\n",
      "         0.0207, -0.0491, -0.0478, -0.0167,  0.0728, -0.0579,  0.0336, -0.0571,\n",
      "        -0.0068,  0.0153, -0.0147,  0.0147,  0.0329,  0.0109,  0.0279,  0.0052,\n",
      "         0.0274,  0.0843,  0.0586,  0.0403, -0.0130,  0.0475, -0.0048, -0.0137,\n",
      "        -0.0087,  0.0625, -0.0157, -0.0541, -0.0112,  0.0987, -0.0224,  0.0192,\n",
      "        -0.0334, -0.0334,  0.0449, -0.0564, -0.0103, -0.0255, -0.0304,  0.0888,\n",
      "         0.0206,  0.0274,  0.0435, -0.0143, -0.0473, -0.0227,  0.0441, -0.0567],\n",
      "       requires_grad=True)), ('features.6.block.2.fc2.scale', tensor(0.0564)), ('features.6.block.2.fc2.zero_point', tensor(60)), ('features.6.block.2.skip_mul.scale', tensor(0.0788)), ('features.6.block.2.skip_mul.zero_point', tensor(0)), ('features.6.block.3.0.weight', tensor([[[[-0.2010]],\n",
      "\n",
      "         [[-0.3617]],\n",
      "\n",
      "         [[-0.1780]],\n",
      "\n",
      "         ...,\n",
      "\n",
      "         [[-0.2124]],\n",
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      "         [[-0.2239]],\n",
      "\n",
      "         [[-0.1665]]],\n",
      "\n",
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      "        [[[ 0.2897]],\n",
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      "         [[ 0.2552]],\n",
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      "         [[-0.0966]],\n",
      "\n",
      "         ...,\n",
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      "         [[ 0.3794]],\n",
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      "         [[ 0.5863]],\n",
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      "         [[-0.2483]]],\n",
      "\n",
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      "        [[[-0.5707]],\n",
      "\n",
      "         [[-0.0308]],\n",
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      "         [[ 0.3008]],\n",
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      "         ...,\n",
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      "         [[ 0.4241]],\n",
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      "         [[-0.0386]],\n",
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      "         [[ 0.1388]]],\n",
      "\n",
      "\n",
      "        ...,\n",
      "\n",
      "\n",
      "        [[[-0.2739]],\n",
      "\n",
      "         [[ 0.3898]],\n",
      "\n",
      "         [[-0.0685]],\n",
      "\n",
      "         ...,\n",
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      "         [[-0.2055]],\n",
      "\n",
      "         [[ 0.1738]],\n",
      "\n",
      "         [[ 0.1370]]],\n",
      "\n",
      "\n",
      "        [[[ 0.0922]],\n",
      "\n",
      "         [[-0.1476]],\n",
      "\n",
      "         [[ 0.0061]],\n",
      "\n",
      "         ...,\n",
      "\n",
      "         [[ 0.1168]],\n",
      "\n",
      "         [[ 0.0307]],\n",
      "\n",
      "         [[-0.7747]]],\n",
      "\n",
      "\n",
      "        [[[-0.0147]],\n",
      "\n",
      "         [[-0.1174]],\n",
      "\n",
      "         [[ 0.1761]],\n",
      "\n",
      "         ...,\n",
      "\n",
      "         [[-0.0587]],\n",
      "\n",
      "         [[ 0.0734]],\n",
      "\n",
      "         [[-0.1541]]]], size=(40, 120, 1, 1), dtype=torch.qint8,\n",
      "       quantization_scheme=torch.per_channel_affine,\n",
      "       scale=tensor([0.0057, 0.0069, 0.0077, 0.0064, 0.0063, 0.0052, 0.0054, 0.0062, 0.0061,\n",
      "        0.0056, 0.0063, 0.0066, 0.0046, 0.0072, 0.0062, 0.0058, 0.0075, 0.0054,\n",
      "        0.0079, 0.0067, 0.0096, 0.0051, 0.0053, 0.0057, 0.0058, 0.0043, 0.0069,\n",
      "        0.0047, 0.0064, 0.0044, 0.0035, 0.0062, 0.0054, 0.0051, 0.0076, 0.0058,\n",
      "        0.0077, 0.0053, 0.0061, 0.0073], dtype=torch.float64),\n",
      "       zero_point=tensor([0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0,\n",
      "        0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0]),\n",
      "       axis=0)), ('features.6.block.3.0.bias', Parameter containing:\n",
      "tensor([-0.1093, -1.0909,  0.2828, -0.9110,  0.0235, -0.9426,  0.2654, -0.8035,\n",
      "        -0.6416,  0.9355,  0.8510,  0.0026, -0.9008,  0.2008,  1.0328, -0.4238,\n",
      "        -0.1469,  0.0055, -0.0662,  0.3894,  0.4713, -0.3705, -0.2189,  0.5625,\n",
      "        -0.2974,  0.0591, -0.5013,  0.1239,  0.2649, -0.1644, -0.4473,  0.2931,\n",
      "        -0.0284, -0.8596,  0.7027,  1.2215, -0.2135, -0.2127,  1.0706, -0.7766])), ('features.6.block.3.0.scale', tensor(0.1674)), ('features.6.block.3.0.zero_point', tensor(64)), ('features.6.skip_add.scale', tensor(0.2694)), ('features.6.skip_add.zero_point', tensor(62)), ('features.7.block.0.0.weight', tensor([[[[ 0.1146]],\n",
      "\n",
      "         [[-0.0777]],\n",
      "\n",
      "         [[ 0.2195]],\n",
      "\n",
      "         ...,\n",
      "\n",
      "         [[ 0.0583]],\n",
      "\n",
      "         [[ 0.0078]],\n",
      "\n",
      "         [[-0.1010]]],\n",
      "\n",
      "\n",
      "        [[[-0.0871]],\n",
      "\n",
      "         [[-0.0843]],\n",
      "\n",
      "         [[-0.0393]],\n",
      "\n",
      "         ...,\n",
      "\n",
      "         [[-0.1194]],\n",
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      "         [[-0.0084]],\n",
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      "         [[-0.1798]]],\n",
      "\n",
      "\n",
      "        [[[-0.0957]],\n",
      "\n",
      "         [[-0.0583]],\n",
      "\n",
      "         [[-0.0359]],\n",
      "\n",
      "         ...,\n",
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      "         [[ 0.0090]]],\n",
      "\n",
      "\n",
      "        ...,\n",
      "\n",
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      "        [[[ 0.0110]],\n",
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      "         [[ 0.0595]],\n",
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      "         [[-0.0859]],\n",
      "\n",
      "         ...,\n",
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      "         [[-0.0044]],\n",
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      "         [[ 0.0617]]],\n",
      "\n",
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      "        [[[ 0.0530]],\n",
      "\n",
      "         [[-0.2008]],\n",
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      "         [[ 0.1326]],\n",
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      "         ...,\n",
      "\n",
      "         [[ 0.0208]],\n",
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      "         [[ 0.1780]],\n",
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      "         [[ 0.1269]]],\n",
      "\n",
      "\n",
      "        [[[-0.0198]],\n",
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      "         [[ 0.0186]],\n",
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      "         ...,\n",
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      "         [[-0.0035]],\n",
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      "         [[ 0.0664]]]], size=(240, 40, 1, 1), dtype=torch.qint8,\n",
      "       quantization_scheme=torch.per_channel_affine,\n",
      "       scale=tensor([0.0019, 0.0014, 0.0015, 0.0012, 0.0018, 0.0012, 0.0016, 0.0014, 0.0015,\n",
      "        0.0014, 0.0012, 0.0015, 0.0015, 0.0020, 0.0021, 0.0010, 0.0018, 0.0015,\n",
      "        0.0019, 0.0015, 0.0019, 0.0019, 0.0018, 0.0021, 0.0020, 0.0011, 0.0019,\n",
      "        0.0015, 0.0011, 0.0017, 0.0017, 0.0011, 0.0016, 0.0012, 0.0017, 0.0019,\n",
      "        0.0017, 0.0017, 0.0018, 0.0019, 0.0022, 0.0018, 0.0017, 0.0016, 0.0019,\n",
      "        0.0021, 0.0014, 0.0012, 0.0016, 0.0019, 0.0017, 0.0016, 0.0025, 0.0034,\n",
      "        0.0024, 0.0016, 0.0012, 0.0020, 0.0013, 0.0013, 0.0020, 0.0015, 0.0014,\n",
      "        0.0020, 0.0011, 0.0020, 0.0016, 0.0016, 0.0011, 0.0019, 0.0017, 0.0019,\n",
      "        0.0021, 0.0015, 0.0020, 0.0012, 0.0014, 0.0011, 0.0018, 0.0017, 0.0019,\n",
      "        0.0014, 0.0018, 0.0011, 0.0011, 0.0018, 0.0014, 0.0016, 0.0013, 0.0023,\n",
      "        0.0015, 0.0013, 0.0012, 0.0015, 0.0013, 0.0021, 0.0020, 0.0015, 0.0014,\n",
      "        0.0014, 0.0024, 0.0015, 0.0010, 0.0018, 0.0018, 0.0016, 0.0016, 0.0011,\n",
      "        0.0017, 0.0014, 0.0016, 0.0012, 0.0009, 0.0012, 0.0022, 0.0016, 0.0010,\n",
      "        0.0013, 0.0018, 0.0015, 0.0020, 0.0013, 0.0009, 0.0018, 0.0011, 0.0025,\n",
      "        0.0025, 0.0014, 0.0010, 0.0019, 0.0012, 0.0016, 0.0019, 0.0024, 0.0017,\n",
      "        0.0016, 0.0021, 0.0010, 0.0024, 0.0024, 0.0021, 0.0014, 0.0018, 0.0013,\n",
      "        0.0014, 0.0012, 0.0030, 0.0015, 0.0012, 0.0013, 0.0017, 0.0011, 0.0018,\n",
      "        0.0012, 0.0018, 0.0014, 0.0010, 0.0014, 0.0018, 0.0017, 0.0015, 0.0017,\n",
      "        0.0010, 0.0013, 0.0016, 0.0017, 0.0010, 0.0013, 0.0016, 0.0016, 0.0018,\n",
      "        0.0018, 0.0019, 0.0024, 0.0017, 0.0013, 0.0015, 0.0018, 0.0021, 0.0024,\n",
      "        0.0015, 0.0015, 0.0015, 0.0018, 0.0013, 0.0025, 0.0015, 0.0016, 0.0020,\n",
      "        0.0016, 0.0017, 0.0017, 0.0020, 0.0011, 0.0014, 0.0011, 0.0014, 0.0016,\n",
      "        0.0014, 0.0012, 0.0011, 0.0015, 0.0019, 0.0015, 0.0014, 0.0018, 0.0014,\n",
      "        0.0012, 0.0014, 0.0021, 0.0018, 0.0017, 0.0011, 0.0013, 0.0014, 0.0010,\n",
      "        0.0012, 0.0017, 0.0014, 0.0017, 0.0012, 0.0010, 0.0016, 0.0014, 0.0024,\n",
      "        0.0020, 0.0012, 0.0016, 0.0013, 0.0022, 0.0017, 0.0021, 0.0014, 0.0014,\n",
      "        0.0015, 0.0015, 0.0016, 0.0022, 0.0019, 0.0012], dtype=torch.float64),\n",
      "       zero_point=tensor([0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0,\n",
      "        0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0,\n",
      "        0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0,\n",
      "        0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0,\n",
      "        0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0,\n",
      "        0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0,\n",
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      "        0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0,\n",
      "        0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0,\n",
      "        0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0]),\n",
      "       axis=0)), ('features.7.block.0.0.bias', Parameter containing:\n",
      "tensor([-0.0196, -0.0621,  0.0663, -0.0041,  0.0787, -0.0081, -0.1259,  0.0184,\n",
      "         0.0655, -0.0046,  0.0520, -0.0915,  0.0935,  0.0259,  0.0027,  0.0354,\n",
      "        -0.1073, -0.0782,  0.1047, -0.0887,  0.0864,  0.0512,  0.0081, -0.0091,\n",
      "        -0.0847, -0.0402,  0.0595, -0.0377,  0.0738,  0.0174,  0.0761,  0.0936,\n",
      "        -0.0344, -0.0917, -0.0170,  0.0986, -0.0362,  0.0015,  0.0551,  0.1096,\n",
      "        -0.0349, -0.0652, -0.1344,  0.0587, -0.0536, -0.0323,  0.0113, -0.0245,\n",
      "        -0.0244, -0.0605,  0.0735,  0.0013,  0.0176,  0.0003, -0.0274, -0.0270,\n",
      "         0.0581, -0.0776,  0.0557,  0.0216, -0.0443, -0.0148,  0.0097, -0.0185,\n",
      "        -0.0112,  0.0215,  0.1313,  0.0981,  0.0317,  0.0639,  0.1606, -0.0794,\n",
      "        -0.0517,  0.1114,  0.0017, -0.0499, -0.0330,  0.0027,  0.0231,  0.0179,\n",
      "         0.0198,  0.1132,  0.0547,  0.0263, -0.0344, -0.0166, -0.0964,  0.0366,\n",
      "         0.0807,  0.0277,  0.0042, -0.0477, -0.0099, -0.1329, -0.0870,  0.0392,\n",
      "        -0.0218,  0.0088, -0.0243,  0.0044, -0.0571, -0.0492, -0.0052, -0.0246,\n",
      "         0.0638,  0.0336,  0.0702, -0.0816,  0.0390, -0.0679, -0.0699, -0.0765,\n",
      "         0.0068, -0.0336, -0.0786,  0.0515,  0.0227, -0.0388, -0.0083,  0.0520,\n",
      "        -0.0380,  0.0073,  0.0314,  0.0596, -0.0589, -0.0457, -0.0452,  0.0466,\n",
      "         0.0847,  0.0797,  0.0387, -0.0087, -0.0654,  0.0561,  0.0074, -0.0346,\n",
      "         0.0749,  0.0189, -0.0200,  0.0384, -0.0418, -0.0275, -0.0191,  0.0091,\n",
      "        -0.0974,  0.0360,  0.0161,  0.0701, -0.0648,  0.0855,  0.0043,  0.0354,\n",
      "         0.0245, -0.0787, -0.0158, -0.0055,  0.0063,  0.0540,  0.0763, -0.1109,\n",
      "         0.0050, -0.0452,  0.0200,  0.0713, -0.0110, -0.0798,  0.0609,  0.0036,\n",
      "        -0.0090,  0.0264,  0.0322, -0.1297,  0.1030, -0.1405,  0.0070, -0.0863,\n",
      "        -0.0658, -0.0441,  0.0106,  0.0442,  0.0659, -0.0846, -0.0052, -0.1008,\n",
      "        -0.0993, -0.0231,  0.0366,  0.0218, -0.0129,  0.0398, -0.1395, -0.0276,\n",
      "         0.0297,  0.1273,  0.0170,  0.0123,  0.0356,  0.0801,  0.0607,  0.0273,\n",
      "        -0.0700,  0.0351,  0.0619,  0.0443, -0.0142,  0.0766,  0.0243, -0.0107,\n",
      "        -0.0106,  0.0131, -0.0297, -0.0761,  0.0186,  0.0483, -0.0465, -0.0922,\n",
      "        -0.0992, -0.0845, -0.0940, -0.0138,  0.0526, -0.0441, -0.0400, -0.0182,\n",
      "        -0.0057, -0.0824,  0.0683, -0.0317, -0.0572, -0.1524, -0.0583, -0.0394,\n",
      "        -0.0549, -0.0580, -0.1422, -0.0439, -0.0270, -0.0695,  0.0216,  0.0561])), ('features.7.block.0.0.scale', tensor(0.1638)), ('features.7.block.0.0.zero_point', tensor(60)), ('features.7.block.0.2.scale', tensor(0.0843)), ('features.7.block.0.2.zero_point', tensor(4)), ('features.7.block.1.0.weight', tensor([[[[-1.3363, -0.2630,  0.8733],\n",
      "          [-0.3577,  0.2315, -1.1258],\n",
      "          [-0.3367,  0.0421, -0.3472]]],\n",
      "\n",
      "\n",
      "        [[[-1.2878,  0.3272,  0.3272],\n",
      "          [-0.1056, -1.2562, -0.6017],\n",
      "          [-0.0633,  0.3167,  0.2322]]],\n",
      "\n",
      "\n",
      "        [[[ 0.1639, -0.0328,  0.8195],\n",
      "          [ 0.3824, -0.0546,  0.4043],\n",
      "          [-1.3986, -0.2950,  0.1530]]],\n",
      "\n",
      "\n",
      "        ...,\n",
      "\n",
      "\n",
      "        [[[-0.3916, -0.1343, -0.9845],\n",
      "          [-0.0671, -0.1454,  0.3916],\n",
      "          [ 0.0112,  1.4208, -0.7160]]],\n",
      "\n",
      "\n",
      "        [[[-0.0324, -0.1836, -0.2699],\n",
      "          [ 0.0000, -0.4103, -0.1188],\n",
      "          [ 1.3713,  1.0042,  0.2267]]],\n",
      "\n",
      "\n",
      "        [[[ 0.1272,  0.0636,  1.0098],\n",
      "          [ 0.2624,  0.4055,  0.2942],\n",
      "          [ 0.0716,  0.3658, -0.1193]]]], size=(240, 1, 3, 3),\n",
      "       dtype=torch.qint8, quantization_scheme=torch.per_channel_affine,\n",
      "       scale=tensor([0.0105, 0.0106, 0.0109, 0.0043, 0.0089, 0.0045, 0.0075, 0.0091, 0.0118,\n",
      "        0.0063, 0.0083, 0.0090, 0.0078, 0.0079, 0.0150, 0.0044, 0.0068, 0.0104,\n",
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      "        0.0088, 0.0052, 0.0056, 0.0088, 0.0100, 0.0041, 0.0093, 0.0090, 0.0092,\n",
      "        0.0073, 0.0096, 0.0103, 0.0114, 0.0115, 0.0102, 0.0125, 0.0052, 0.0066,\n",
      "        0.0044, 0.0059, 0.0159, 0.0054, 0.0075, 0.0040, 0.0116, 0.0138, 0.0066,\n",
      "        0.0087, 0.0129, 0.0062, 0.0075, 0.0099, 0.0089, 0.0067, 0.0048, 0.0106,\n",
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      "        0.0074, 0.0057, 0.0074, 0.0064, 0.0097, 0.0077, 0.0106, 0.0066, 0.0048,\n",
      "        0.0047, 0.0130, 0.0111, 0.0119, 0.0160, 0.0099, 0.0186, 0.0069, 0.0150,\n",
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      "        0.0045, 0.0082, 0.0080, 0.0146, 0.0039, 0.0074, 0.0089, 0.0055, 0.0081,\n",
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      "        0.0058, 0.0179, 0.0052, 0.0130, 0.0076, 0.0222, 0.0069, 0.0103, 0.0085,\n",
      "        0.0086, 0.0041, 0.0156, 0.0162, 0.0073, 0.0070, 0.0044, 0.0156, 0.0087,\n",
      "        0.0128, 0.0087, 0.0083, 0.0112, 0.0108, 0.0080], dtype=torch.float64),\n",
      "       zero_point=tensor([0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0,\n",
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      "       axis=0)), ('features.7.block.1.0.bias', Parameter containing:\n",
      "tensor([ 0.3155,  0.2405, -0.0188,  0.1600,  0.4615, -0.2422, -0.2387, -0.3762,\n",
      "        -0.0833, -0.2760, -0.1727, -0.1646,  0.2083, -0.1829,  0.1006,  0.2952,\n",
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      "        -0.2633, -0.3808, -0.3571, -0.0430,  0.1756,  0.3389,  0.3804, -0.0251,\n",
      "         0.0669, -0.2478, -0.0147, -0.2511, -0.3768,  0.3220,  0.1294, -0.2004,\n",
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      "        -0.2246, -0.2368, -0.0988, -0.1754,  0.5570, -0.2743,  0.2515,  0.3012,\n",
      "         0.3167, -0.3222,  0.2124, -0.1854,  0.5671,  0.3118, -0.2344,  0.0971,\n",
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      "         [[-0.0532]],\n",
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      "       quantization_scheme=torch.per_channel_affine,\n",
      "       scale=tensor([0.0021, 0.0022, 0.0032, 0.0026, 0.0021, 0.0017, 0.0017, 0.0026, 0.0014,\n",
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      "       dtype=torch.float64),\n",
      "       zero_point=tensor([0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0,\n",
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      "        0, 0, 0, 0, 0, 0, 0, 0]),\n",
      "       axis=0)), ('features.7.block.2.0.bias', Parameter containing:\n",
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      "         0.0791, -0.3099, -0.1478,  0.0277, -0.0759,  0.1261, -0.0100, -0.0689,\n",
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      "\n",
      "         [[-0.0373]],\n",
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      "         ...,\n",
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      "       quantization_scheme=torch.per_channel_affine,\n",
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      "        0, 0, 0, 0, 0, 0, 0, 0]),\n",
      "       axis=0)), ('features.8.block.0.0.bias', Parameter containing:\n",
      "tensor([ 0.0133,  0.0293,  0.0685, -0.0029,  0.0430, -0.0658, -0.0894, -0.0495,\n",
      "         0.0600,  0.0485,  0.0872, -0.0491, -0.0150, -0.0820, -0.0281, -0.1487,\n",
      "        -0.1507,  0.0304,  0.1405, -0.0167,  0.0583, -0.0613,  0.0202,  0.1113,\n",
      "         0.0764, -0.0757,  0.0442,  0.0863, -0.0408,  0.0782, -0.0851, -0.0230,\n",
      "        -0.0121,  0.0063, -0.0751,  0.0729,  0.0039,  0.0121, -0.0754,  0.0825,\n",
      "         0.1087,  0.0805, -0.0697,  0.0763,  0.0607,  0.0202, -0.0545,  0.0274,\n",
      "        -0.0023,  0.0438,  0.0609, -0.0511, -0.0061,  0.0429,  0.0110, -0.0532,\n",
      "        -0.0047, -0.0324,  0.0951,  0.0051,  0.0493,  0.0229,  0.0229, -0.0950,\n",
      "         0.0533, -0.0718,  0.0374, -0.0130,  0.0273,  0.1090,  0.1120,  0.0942,\n",
      "         0.0320,  0.0465,  0.0350, -0.1162, -0.0544,  0.0287, -0.0630,  0.0284,\n",
      "         0.0263,  0.0451, -0.0200, -0.0623,  0.0862, -0.1539,  0.0494, -0.0211,\n",
      "         0.0957,  0.0294,  0.0059,  0.0236,  0.0266, -0.0381, -0.0207,  0.0375,\n",
      "         0.0433, -0.1397,  0.0325, -0.0216, -0.0465,  0.0366, -0.0495,  0.0185,\n",
      "        -0.0202, -0.0423, -0.0085,  0.0185,  0.0456,  0.0714, -0.0264,  0.0559,\n",
      "         0.0404, -0.0168,  0.0764, -0.0469, -0.0908,  0.0224, -0.0038, -0.0916,\n",
      "        -0.0250,  0.0267, -0.0701, -0.0133,  0.0446,  0.0436, -0.0210, -0.1260,\n",
      "         0.0737, -0.0482,  0.0769,  0.0507, -0.0406,  0.0903, -0.0524,  0.0488,\n",
      "        -0.0280,  0.0227, -0.0285,  0.0176, -0.1358,  0.0439, -0.1058, -0.0054,\n",
      "        -0.0800,  0.0039, -0.0383, -0.0056,  0.0397, -0.0118, -0.0006, -0.0375,\n",
      "         0.0219,  0.0273,  0.0288, -0.0455, -0.0277,  0.0479, -0.0865, -0.0067,\n",
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      "         0.1115, -0.0782,  0.0083, -0.0313,  0.0294, -0.0016, -0.0229,  0.1067,\n",
      "         0.0527,  0.0476, -0.0333,  0.0516, -0.0067, -0.0182, -0.0070, -0.0153])), ('features.8.block.0.0.scale', tensor(0.1941)), ('features.8.block.0.0.zero_point', tensor(61)), ('features.8.block.0.2.scale', tensor(0.0968)), ('features.8.block.0.2.zero_point', tensor(4)), ('features.8.block.1.0.weight', tensor([[[[-0.3583, -0.0796,  0.6469],\n",
      "          [-1.2738,  0.2886,  0.6568],\n",
      "          [-0.0199, -0.1393,  0.3782]]],\n",
      "\n",
      "\n",
      "        [[[-0.0975, -0.0928, -0.3899],\n",
      "          [-0.4735, -0.1114, -0.0186],\n",
      "          [-0.5756, -0.0511, -0.1996]]],\n",
      "\n",
      "\n",
      "        [[[-0.0677,  0.0414, -0.0414],\n",
      "          [ 0.4475,  0.0226, -0.0639],\n",
      "          [ 0.2256,  0.4400,  0.3723]]],\n",
      "\n",
      "\n",
      "        ...,\n",
      "\n",
      "\n",
      "        [[[ 0.0610,  0.0839,  0.9691],\n",
      "          [ 0.1297,  0.1755,  0.1908],\n",
      "          [-0.9767, -0.0305,  0.3129]]],\n",
      "\n",
      "\n",
      "        [[[ 0.1445,  0.7617, -0.2101],\n",
      "          [-0.3152,  0.7748,  1.6678],\n",
      "          [-1.2607, -0.4859, -0.4990]]],\n",
      "\n",
      "\n",
      "        [[[ 0.1795, -0.0598, -0.0150],\n",
      "          [-0.9274, -0.2468, -0.4188],\n",
      "          [-0.0075, -0.6955,  0.0299]]]], size=(200, 1, 3, 3),\n",
      "       dtype=torch.qint8, quantization_scheme=torch.per_channel_affine,\n",
      "       scale=tensor([0.0100, 0.0046, 0.0038, 0.0084, 0.0082, 0.0160, 0.0129, 0.0110, 0.0042,\n",
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      "        0.0055, 0.0081, 0.0034, 0.0192, 0.0086, 0.0101, 0.0041, 0.0139, 0.0058,\n",
      "        0.0096, 0.0091, 0.0081, 0.0216, 0.0174, 0.0115, 0.0039, 0.0130, 0.0048,\n",
      "        0.0219, 0.0066, 0.0093, 0.0056, 0.0064, 0.0093, 0.0139, 0.0084, 0.0038,\n",
      "        0.0114, 0.0223, 0.0073, 0.0117, 0.0114, 0.0125, 0.0085, 0.0087, 0.0089,\n",
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      "        0.0203, 0.0123, 0.0071, 0.0050, 0.0077, 0.0113, 0.0045, 0.0071, 0.0050,\n",
      "        0.0085, 0.0126, 0.0045, 0.0133, 0.0052, 0.0072, 0.0140, 0.0208, 0.0076,\n",
      "        0.0044, 0.0113, 0.0120, 0.0025, 0.0140, 0.0074, 0.0130, 0.0043, 0.0040,\n",
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      "        0.0105, 0.0038, 0.0114, 0.0075, 0.0104, 0.0167, 0.0062, 0.0064, 0.0121,\n",
      "        0.0082, 0.0129, 0.0079, 0.0146, 0.0070, 0.0083, 0.0116, 0.0066, 0.0117,\n",
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      "        0.0200, 0.0091, 0.0052, 0.0062, 0.0070, 0.0037, 0.0133, 0.0151, 0.0050,\n",
      "        0.0068, 0.0135, 0.0103, 0.0091, 0.0090, 0.0229, 0.0052, 0.0056, 0.0093,\n",
      "        0.0085, 0.0076, 0.0064, 0.0069, 0.0057, 0.0075, 0.0150, 0.0063, 0.0119,\n",
      "        0.0043, 0.0096, 0.0121, 0.0101, 0.0103, 0.0060, 0.0118, 0.0089, 0.0118,\n",
      "        0.0118, 0.0083, 0.0104, 0.0083, 0.0091, 0.0214, 0.0076, 0.0118, 0.0042,\n",
      "        0.0047, 0.0092, 0.0043, 0.0047, 0.0078, 0.0063, 0.0051, 0.0096, 0.0076,\n",
      "        0.0131, 0.0075], dtype=torch.float64),\n",
      "       zero_point=tensor([0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0,\n",
      "        0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0,\n",
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      "        0, 0, 0, 0, 0, 0, 0, 0]),\n",
      "       axis=0)), ('features.8.block.1.0.bias', Parameter containing:\n",
      "tensor([ 0.0198,  0.2374, -0.2144, -0.0043, -0.1257, -0.2221,  0.1284,  0.0542,\n",
      "         0.1806, -0.3715,  0.2102, -0.1548,  0.1532,  0.1060,  0.5437, -0.0153,\n",
      "        -0.1630, -0.1539,  0.1423, -0.0132, -0.1514,  0.0926,  0.1090, -0.2936,\n",
      "        -0.1694,  0.2709,  0.1013,  0.4711, -0.2163, -0.1777, -0.1007, -0.0518,\n",
      "         0.1843, -0.1671,  0.2108, -0.1655,  0.2976, -0.2221, -0.2546,  0.2617,\n",
      "         0.5726,  0.1053,  0.2283, -0.0812,  0.1448,  0.0119, -0.0079, -0.1273,\n",
      "        -0.0338, -0.1225,  0.2021,  0.3655, -0.0546,  0.3647,  0.0282,  0.4407,\n",
      "        -0.1039,  0.4336,  0.2605,  0.4014, -0.1833,  0.0862, -0.2444,  0.1427,\n",
      "         0.0886,  0.3385, -0.1354,  0.0213,  0.0455, -0.1983,  0.0888,  0.1728,\n",
      "         0.0093,  0.3303, -0.1469,  0.3218, -0.2415,  0.2360, -0.0227, -0.2817,\n",
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      "        -0.1067,  0.0793,  0.1301,  0.2318,  0.4610, -0.1335,  0.0780, -0.0117,\n",
      "        -0.2308,  0.2017,  0.2656, -0.2501,  0.2805,  0.0008, -0.3071, -0.1323,\n",
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      "         0.1014, -0.2518,  0.2714, -0.3839, -0.1379,  0.1711,  0.4719,  0.0984,\n",
      "         0.2021, -0.2844,  0.1550,  0.2497, -0.3576, -0.1231,  0.0016,  0.2500])), ('features.8.block.1.0.scale', tensor(0.2377)), ('features.8.block.1.0.zero_point', tensor(65)), ('features.8.block.1.2.scale', tensor(0.1136)), ('features.8.block.1.2.zero_point', tensor(3)), ('features.8.block.2.0.weight', tensor([[[[-0.0636]],\n",
      "\n",
      "         [[ 0.0106]],\n",
      "\n",
      "         [[-0.0297]],\n",
      "\n",
      "         ...,\n",
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      "         [[ 0.1040]],\n",
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      "         [[ 0.1146]]],\n",
      "\n",
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      "        [[[-0.0344]],\n",
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      "         ...,\n",
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      "         [[ 0.0109]]],\n",
      "\n",
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      "        [[[ 0.0093]],\n",
      "\n",
      "         [[ 0.0301]],\n",
      "\n",
      "         [[ 0.0208]],\n",
      "\n",
      "         ...,\n",
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      "         [[-0.0116]],\n",
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      "         [[-0.0833]]],\n",
      "\n",
      "\n",
      "        ...,\n",
      "\n",
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      "         ...,\n",
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      "         [[-0.0997]],\n",
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      "         [[-0.0131]],\n",
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      "         [[-0.0184]]],\n",
      "\n",
      "\n",
      "        [[[ 0.0427]],\n",
      "\n",
      "         [[ 0.0995]],\n",
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      "\n",
      "         ...,\n",
      "\n",
      "         [[ 0.0406]],\n",
      "\n",
      "         [[ 0.0894]],\n",
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      "         [[-0.1219]]],\n",
      "\n",
      "\n",
      "        [[[-0.0509]],\n",
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      "         [[ 0.0170]],\n",
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      "         [[-0.1499]],\n",
      "\n",
      "         ...,\n",
      "\n",
      "         [[ 0.1075]],\n",
      "\n",
      "         [[-0.0028]],\n",
      "\n",
      "         [[-0.1471]]]], size=(80, 200, 1, 1), dtype=torch.qint8,\n",
      "       quantization_scheme=torch.per_channel_affine,\n",
      "       scale=tensor([0.0021, 0.0016, 0.0023, 0.0014, 0.0018, 0.0022, 0.0021, 0.0018, 0.0024,\n",
      "        0.0016, 0.0032, 0.0024, 0.0023, 0.0025, 0.0016, 0.0023, 0.0023, 0.0016,\n",
      "        0.0017, 0.0015, 0.0033, 0.0018, 0.0023, 0.0025, 0.0021, 0.0022, 0.0022,\n",
      "        0.0032, 0.0037, 0.0026, 0.0024, 0.0032, 0.0019, 0.0030, 0.0022, 0.0036,\n",
      "        0.0015, 0.0021, 0.0015, 0.0019, 0.0016, 0.0031, 0.0013, 0.0028, 0.0026,\n",
      "        0.0028, 0.0024, 0.0019, 0.0023, 0.0017, 0.0029, 0.0024, 0.0019, 0.0022,\n",
      "        0.0026, 0.0017, 0.0032, 0.0027, 0.0020, 0.0024, 0.0033, 0.0018, 0.0026,\n",
      "        0.0018, 0.0024, 0.0033, 0.0035, 0.0022, 0.0027, 0.0018, 0.0019, 0.0028,\n",
      "        0.0024, 0.0021, 0.0031, 0.0013, 0.0015, 0.0026, 0.0020, 0.0028],\n",
      "       dtype=torch.float64),\n",
      "       zero_point=tensor([0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0,\n",
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      "        0, 0, 0, 0, 0, 0, 0, 0]),\n",
      "       axis=0)), ('features.8.block.2.0.bias', Parameter containing:\n",
      "tensor([ 0.1667,  0.3927, -0.1569,  0.4789, -0.0416, -0.1621,  0.0799, -0.2385,\n",
      "         0.3149,  0.3312, -0.1876,  0.0987, -0.3533,  0.1478, -0.0974,  0.4018,\n",
      "        -0.1740,  0.2917,  0.2194, -0.2857,  0.2034, -0.1795,  0.2302,  0.2880,\n",
      "         0.0615, -0.2869,  0.1834,  0.0737, -0.0164,  0.0426,  0.1285, -0.0309,\n",
      "         0.1169, -0.0808, -0.2276,  0.0915, -0.3657, -0.1347, -0.0147,  0.1568,\n",
      "         0.0997, -0.1831, -0.3637,  0.0729, -0.0457,  0.0097,  0.0169, -0.1833,\n",
      "         0.0754, -0.1839,  0.1146,  0.4724,  0.0641,  0.1402,  0.1413,  0.2354,\n",
      "        -0.2234,  0.1834,  0.1408, -0.1426, -0.1516,  0.2075, -0.2328,  0.0502,\n",
      "         0.3368, -0.1961, -0.5545, -0.1297,  0.0157, -0.2542, -0.3103, -0.0923,\n",
      "        -0.0827,  0.2671, -0.1203, -0.1475,  0.3218,  0.4294, -0.1506, -0.1423])), ('features.8.block.2.0.scale', tensor(0.1994)), ('features.8.block.2.0.zero_point', tensor(63)), ('features.8.skip_add.scale', tensor(0.3240)), ('features.8.skip_add.zero_point', tensor(64)), ('features.9.block.0.0.weight', tensor([[[[ 0.0108]],\n",
      "\n",
      "         [[-0.0495]],\n",
      "\n",
      "         [[-0.0215]],\n",
      "\n",
      "         ...,\n",
      "\n",
      "         [[-0.0416]],\n",
      "\n",
      "         [[ 0.0022]],\n",
      "\n",
      "         [[-0.0488]]],\n",
      "\n",
      "\n",
      "        [[[-0.0780]],\n",
      "\n",
      "         [[ 0.0312]],\n",
      "\n",
      "         [[-0.0551]],\n",
      "\n",
      "         ...,\n",
      "\n",
      "         [[ 0.0021]],\n",
      "\n",
      "         [[-0.0208]],\n",
      "\n",
      "         [[-0.0042]]],\n",
      "\n",
      "\n",
      "        [[[-0.0466]],\n",
      "\n",
      "         [[-0.0622]],\n",
      "\n",
      "         [[-0.0276]],\n",
      "\n",
      "         ...,\n",
      "\n",
      "         [[-0.1053]],\n",
      "\n",
      "         [[-0.0017]],\n",
      "\n",
      "         [[-0.0967]]],\n",
      "\n",
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      "        ...,\n",
      "\n",
      "\n",
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      "\n",
      "         [[-0.0747]],\n",
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      "\n",
      "         ...,\n",
      "\n",
      "         [[ 0.0721]],\n",
      "\n",
      "         [[ 0.1090]],\n",
      "\n",
      "         [[-0.0781]]],\n",
      "\n",
      "\n",
      "        [[[ 0.0245]],\n",
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      "         [[ 0.0788]],\n",
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      "         ...,\n",
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      "         [[-0.0181]],\n",
      "\n",
      "         [[ 0.0426]],\n",
      "\n",
      "         [[-0.0107]]],\n",
      "\n",
      "\n",
      "        [[[ 0.0146]],\n",
      "\n",
      "         [[ 0.0303]],\n",
      "\n",
      "         [[ 0.0483]],\n",
      "\n",
      "         ...,\n",
      "\n",
      "         [[ 0.0988]],\n",
      "\n",
      "         [[-0.0258]],\n",
      "\n",
      "         [[ 0.0314]]]], size=(184, 80, 1, 1), dtype=torch.qint8,\n",
      "       quantization_scheme=torch.per_channel_affine,\n",
      "       scale=tensor([0.0007, 0.0010, 0.0017, 0.0017, 0.0009, 0.0009, 0.0011, 0.0012, 0.0014,\n",
      "        0.0010, 0.0013, 0.0010, 0.0016, 0.0012, 0.0016, 0.0009, 0.0018, 0.0009,\n",
      "        0.0020, 0.0011, 0.0018, 0.0008, 0.0008, 0.0011, 0.0013, 0.0007, 0.0007,\n",
      "        0.0006, 0.0013, 0.0009, 0.0020, 0.0009, 0.0015, 0.0011, 0.0017, 0.0014,\n",
      "        0.0014, 0.0019, 0.0011, 0.0013, 0.0010, 0.0020, 0.0023, 0.0012, 0.0008,\n",
      "        0.0010, 0.0013, 0.0016, 0.0008, 0.0008, 0.0010, 0.0008, 0.0011, 0.0011,\n",
      "        0.0008, 0.0012, 0.0015, 0.0009, 0.0014, 0.0010, 0.0012, 0.0012, 0.0011,\n",
      "        0.0008, 0.0016, 0.0011, 0.0010, 0.0012, 0.0012, 0.0011, 0.0012, 0.0010,\n",
      "        0.0014, 0.0016, 0.0013, 0.0011, 0.0012, 0.0011, 0.0012, 0.0011, 0.0012,\n",
      "        0.0008, 0.0008, 0.0011, 0.0015, 0.0015, 0.0010, 0.0010, 0.0009, 0.0008,\n",
      "        0.0008, 0.0015, 0.0014, 0.0016, 0.0013, 0.0015, 0.0009, 0.0012, 0.0015,\n",
      "        0.0012, 0.0012, 0.0010, 0.0018, 0.0009, 0.0007, 0.0012, 0.0014, 0.0011,\n",
      "        0.0006, 0.0007, 0.0013, 0.0007, 0.0012, 0.0012, 0.0012, 0.0011, 0.0011,\n",
      "        0.0012, 0.0009, 0.0009, 0.0011, 0.0009, 0.0014, 0.0023, 0.0008, 0.0013,\n",
      "        0.0010, 0.0010, 0.0009, 0.0015, 0.0008, 0.0013, 0.0012, 0.0009, 0.0009,\n",
      "        0.0012, 0.0008, 0.0016, 0.0010, 0.0014, 0.0009, 0.0008, 0.0009, 0.0016,\n",
      "        0.0017, 0.0015, 0.0007, 0.0011, 0.0007, 0.0011, 0.0009, 0.0008, 0.0015,\n",
      "        0.0009, 0.0018, 0.0012, 0.0012, 0.0013, 0.0017, 0.0008, 0.0015, 0.0008,\n",
      "        0.0011, 0.0008, 0.0009, 0.0014, 0.0008, 0.0008, 0.0013, 0.0010, 0.0006,\n",
      "        0.0015, 0.0017, 0.0017, 0.0009, 0.0016, 0.0014, 0.0008, 0.0022, 0.0016,\n",
      "        0.0013, 0.0009, 0.0011, 0.0011], dtype=torch.float64),\n",
      "       zero_point=tensor([0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0,\n",
      "        0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0,\n",
      "        0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0,\n",
      "        0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0,\n",
      "        0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0,\n",
      "        0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0,\n",
      "        0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0,\n",
      "        0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0]),\n",
      "       axis=0)), ('features.9.block.0.0.bias', Parameter containing:\n",
      "tensor([-0.0166, -0.0231, -0.0699, -0.1138, -0.0447,  0.0716,  0.0285, -0.0421,\n",
      "         0.1142,  0.1018,  0.0573, -0.0191, -0.0777, -0.0555, -0.0519,  0.0570,\n",
      "        -0.1690, -0.0355,  0.0148, -0.0104, -0.0356, -0.0777,  0.0575,  0.0457,\n",
      "         0.0232, -0.1065,  0.0404,  0.0150,  0.0603, -0.0267, -0.1185, -0.0519,\n",
      "         0.0014,  0.0333,  0.0356,  0.0511, -0.1245,  0.1266, -0.0582,  0.0965,\n",
      "         0.0696,  0.0460,  0.0329,  0.0649,  0.0803,  0.0509,  0.1108, -0.0203,\n",
      "         0.0105,  0.0761, -0.0383, -0.0707, -0.1588, -0.0155, -0.0271,  0.0872,\n",
      "         0.1313, -0.0380,  0.0556,  0.0436, -0.0462, -0.0374, -0.0723,  0.0469,\n",
      "         0.0494,  0.0709,  0.0004, -0.0668,  0.0729,  0.0594,  0.0322, -0.0915,\n",
      "         0.0081, -0.0734, -0.0664, -0.0685, -0.0560, -0.0996,  0.0182, -0.0170,\n",
      "         0.1354, -0.0499, -0.0217, -0.0437,  0.0497,  0.1270, -0.0199,  0.0297,\n",
      "         0.0793, -0.0932,  0.0220,  0.1211, -0.0784,  0.1013,  0.0189,  0.0190,\n",
      "         0.0403, -0.0234,  0.0041, -0.0390,  0.1334,  0.0554,  0.1233,  0.0125,\n",
      "         0.0356, -0.0746,  0.0966,  0.0183,  0.0722,  0.0186,  0.0592,  0.0825,\n",
      "        -0.0148,  0.0543,  0.0738,  0.0457,  0.1149, -0.1261, -0.0316,  0.0516,\n",
      "        -0.0083, -0.0333,  0.1174, -0.0259,  0.1069,  0.0716,  0.0019,  0.1094,\n",
      "         0.0747,  0.0075,  0.0640, -0.0105,  0.0291, -0.0545,  0.0965, -0.0828,\n",
      "         0.1096, -0.0766,  0.1418,  0.1034, -0.0266,  0.0571, -0.0044, -0.0212,\n",
      "        -0.0438, -0.0202,  0.1163, -0.0032,  0.0506, -0.1346,  0.0154,  0.0431,\n",
      "         0.0684,  0.0824,  0.0587, -0.0673,  0.0143,  0.0261,  0.0672, -0.0060,\n",
      "         0.1064, -0.0894,  0.0284, -0.1141,  0.0463, -0.0850,  0.0884,  0.0200,\n",
      "         0.0218, -0.0180,  0.0615, -0.0410,  0.0496,  0.0335,  0.0091,  0.1374,\n",
      "         0.0242, -0.0039,  0.0952,  0.1246, -0.0020,  0.0149, -0.0751,  0.0327])), ('features.9.block.0.0.scale', tensor(0.2048)), ('features.9.block.0.0.zero_point', tensor(63)), ('features.9.block.0.2.scale', tensor(0.0992)), ('features.9.block.0.2.zero_point', tensor(4)), ('features.9.block.1.0.weight', tensor([[[[ 0.4396,  0.0382,  2.3320],\n",
      "          [-0.7837, -0.5925,  0.0191],\n",
      "          [ 0.0956, -0.7072, -1.9115]]],\n",
      "\n",
      "\n",
      "        [[[-0.2177, -0.5115, -0.4571],\n",
      "          [ 0.5442, -0.6095, -1.3278],\n",
      "          [ 0.0218,  0.5115, -0.7727]]],\n",
      "\n",
      "\n",
      "        [[[-1.5514, -0.1697, -0.0606],\n",
      "          [ 0.0121, -1.2484, -0.0848],\n",
      "          [-0.0970,  0.1576, -0.4242]]],\n",
      "\n",
      "\n",
      "        ...,\n",
      "\n",
      "\n",
      "        [[[ 0.1616,  0.9329,  0.0367],\n",
      "          [ 0.2204, -0.8301, -0.3306],\n",
      "          [ 0.0441,  0.0000, -0.4187]]],\n",
      "\n",
      "\n",
      "        [[[ 0.3941,  0.1314,  0.0910],\n",
      "          [ 1.2431,  0.3739,  0.4042],\n",
      "          [-0.1415,  0.3537, -0.2931]]],\n",
      "\n",
      "\n",
      "        [[[-0.2617, -0.0214,  0.2136],\n",
      "          [ 0.1389,  0.6035,  0.0694],\n",
      "          [ 0.1923,  0.4753,  0.6783]]]], size=(184, 1, 3, 3),\n",
      "       dtype=torch.qint8, quantization_scheme=torch.per_channel_affine,\n",
      "       scale=tensor([0.0191, 0.0109, 0.0121, 0.0107, 0.0179, 0.0054, 0.0044, 0.0099, 0.0055,\n",
      "        0.0066, 0.0098, 0.0057, 0.0122, 0.0082, 0.0096, 0.0042, 0.0150, 0.0152,\n",
      "        0.0062, 0.0136, 0.0116, 0.0190, 0.0043, 0.0088, 0.0164, 0.0196, 0.0152,\n",
      "        0.0055, 0.0062, 0.0172, 0.0088, 0.0085, 0.0098, 0.0121, 0.0085, 0.0104,\n",
      "        0.0097, 0.0057, 0.0212, 0.0124, 0.0045, 0.0107, 0.0077, 0.0038, 0.0072,\n",
      "        0.0072, 0.0128, 0.0116, 0.0050, 0.0034, 0.0057, 0.0115, 0.0140, 0.0050,\n",
      "        0.0149, 0.0064, 0.0073, 0.0165, 0.0140, 0.0067, 0.0049, 0.0068, 0.0262,\n",
      "        0.0086, 0.0085, 0.0105, 0.0155, 0.0199, 0.0151, 0.0092, 0.0060, 0.0262,\n",
      "        0.0099, 0.0137, 0.0239, 0.0146, 0.0069, 0.0096, 0.0093, 0.0109, 0.0025,\n",
      "        0.0226, 0.0106, 0.0160, 0.0101, 0.0077, 0.0164, 0.0201, 0.0078, 0.0218,\n",
      "        0.0185, 0.0097, 0.0162, 0.0083, 0.0096, 0.0071, 0.0115, 0.0116, 0.0062,\n",
      "        0.0083, 0.0055, 0.0053, 0.0072, 0.0063, 0.0101, 0.0139, 0.0090, 0.0069,\n",
      "        0.0032, 0.0104, 0.0107, 0.0069, 0.0055, 0.0132, 0.0066, 0.0088, 0.0038,\n",
      "        0.0146, 0.0199, 0.0043, 0.0054, 0.0096, 0.0065, 0.0046, 0.0065, 0.0144,\n",
      "        0.0081, 0.0074, 0.0055, 0.0096, 0.0039, 0.0045, 0.0062, 0.0069, 0.0062,\n",
      "        0.0086, 0.0030, 0.0110, 0.0090, 0.0056, 0.0207, 0.0121, 0.0167, 0.0093,\n",
      "        0.0097, 0.0115, 0.0064, 0.0089, 0.0045, 0.0133, 0.0039, 0.0059, 0.0047,\n",
      "        0.0077, 0.0087, 0.0046, 0.0080, 0.0039, 0.0103, 0.0137, 0.0074, 0.0191,\n",
      "        0.0042, 0.0186, 0.0077, 0.0036, 0.0053, 0.0041, 0.0068, 0.0103, 0.0060,\n",
      "        0.0117, 0.0080, 0.0096, 0.0053, 0.0066, 0.0090, 0.0063, 0.0054, 0.0119,\n",
      "        0.0042, 0.0073, 0.0101, 0.0053], dtype=torch.float64),\n",
      "       zero_point=tensor([0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0,\n",
      "        0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0,\n",
      "        0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0,\n",
      "        0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0,\n",
      "        0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0,\n",
      "        0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0,\n",
      "        0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0,\n",
      "        0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0]),\n",
      "       axis=0)), ('features.9.block.1.0.bias', Parameter containing:\n",
      "tensor([ 0.0896,  0.3881,  0.4137,  0.1181,  0.3312,  0.1729, -0.1911,  0.1320,\n",
      "         0.2736, -0.0666,  0.2842,  0.0983,  0.3864,  0.1952,  0.1059,  0.1209,\n",
      "        -0.0464,  0.1031,  0.0158,  0.2270,  0.1420,  0.1211, -0.1774,  0.4537,\n",
      "        -0.1717, -0.1758, -0.0945,  0.0972,  0.1626, -0.0668,  0.2630, -0.3558,\n",
      "        -0.1998,  0.1933, -0.0915,  0.0174,  0.1629,  0.2973,  0.1521,  0.2284,\n",
      "         0.2233, -0.0835,  0.1003,  0.2134,  0.1122,  0.1844,  0.1737,  0.1375,\n",
      "         0.1747, -0.1374,  0.1675, -0.4314, -0.1201,  0.2548, -0.1345,  0.0412,\n",
      "         0.0365, -0.3571,  0.2539, -0.0871, -0.2807,  0.1129,  0.0456,  0.0643,\n",
      "        -0.0627,  0.4836, -0.2693,  0.3308, -0.0521, -0.1999,  0.1693,  0.2760,\n",
      "         0.0285, -0.0781, -0.2979, -0.3315,  0.3277,  0.0431,  0.1621, -0.3258,\n",
      "        -0.2161, -0.2206, -0.2250, -0.0551, -0.0598,  0.0559,  0.3030,  0.1519,\n",
      "         0.0598,  0.1430, -0.5668,  0.0974, -0.3195,  0.3778,  0.0055,  0.2102,\n",
      "        -0.2968, -0.2761, -0.2922, -0.0595, -0.1519, -0.1509, -0.0386, -0.1335,\n",
      "        -0.1284,  0.1069,  0.0924, -0.0596, -0.1863,  0.1970, -0.1890,  0.1268,\n",
      "         0.3363,  0.0846, -0.0505, -0.1785, -0.1481,  0.0353,  0.2908,  0.1038,\n",
      "        -0.2768, -0.3190, -0.4715,  0.1861, -0.1685,  0.1119,  0.0661,  0.2002,\n",
      "         0.0621, -0.1002,  0.1492, -0.2012, -0.1758,  0.4817,  0.1138,  0.0860,\n",
      "        -0.2244, -0.0536,  0.1709,  0.2907,  0.0385, -0.1874, -0.0742,  0.3780,\n",
      "        -0.1910,  0.1489, -0.0016,  0.1572, -0.1693, -0.1610,  0.1354,  0.1107,\n",
      "         0.1580, -0.1179, -0.0985, -0.2028, -0.4987,  0.1771,  0.4420,  0.2642,\n",
      "         0.1249,  0.1836, -0.2994,  0.2216, -0.0655, -0.1992, -0.1393, -0.1758,\n",
      "        -0.1702, -0.0038, -0.1314,  0.0715,  0.1187, -0.3032, -0.1725, -0.0446,\n",
      "        -0.1506,  0.2051, -0.1813,  0.0790, -0.1857, -0.0335, -0.2735, -0.2841])), ('features.9.block.1.0.scale', tensor(0.2301)), ('features.9.block.1.0.zero_point', tensor(62)), ('features.9.block.1.2.scale', tensor(0.1136)), ('features.9.block.1.2.zero_point', tensor(3)), ('features.9.block.2.0.weight', tensor([[[[-0.0063]],\n",
      "\n",
      "         [[-0.0211]],\n",
      "\n",
      "         [[ 0.1875]],\n",
      "\n",
      "         ...,\n",
      "\n",
      "         [[ 0.1032]],\n",
      "\n",
      "         [[-0.1159]],\n",
      "\n",
      "         [[ 0.0822]]],\n",
      "\n",
      "\n",
      "        [[[ 0.0640]],\n",
      "\n",
      "         [[-0.0297]],\n",
      "\n",
      "         [[-0.0046]],\n",
      "\n",
      "         ...,\n",
      "\n",
      "         [[ 0.1257]],\n",
      "\n",
      "         [[ 0.0982]],\n",
      "\n",
      "         [[-0.0503]]],\n",
      "\n",
      "\n",
      "        [[[-0.0202]],\n",
      "\n",
      "         [[-0.1472]],\n",
      "\n",
      "         [[-0.0664]],\n",
      "\n",
      "         ...,\n",
      "\n",
      "         [[ 0.1241]],\n",
      "\n",
      "         [[ 0.2251]],\n",
      "\n",
      "         [[ 0.1154]]],\n",
      "\n",
      "\n",
      "        ...,\n",
      "\n",
      "\n",
      "        [[[-0.0939]],\n",
      "\n",
      "         [[ 0.0755]],\n",
      "\n",
      "         [[-0.0534]],\n",
      "\n",
      "         ...,\n",
      "\n",
      "         [[-0.0147]],\n",
      "\n",
      "         [[ 0.0000]],\n",
      "\n",
      "         [[ 0.0258]]],\n",
      "\n",
      "\n",
      "        [[[ 0.0490]],\n",
      "\n",
      "         [[-0.0637]],\n",
      "\n",
      "         [[ 0.0212]],\n",
      "\n",
      "         ...,\n",
      "\n",
      "         [[ 0.0359]],\n",
      "\n",
      "         [[ 0.0572]],\n",
      "\n",
      "         [[ 0.0245]]],\n",
      "\n",
      "\n",
      "        [[[-0.0035]],\n",
      "\n",
      "         [[ 0.1437]],\n",
      "\n",
      "         [[ 0.0876]],\n",
      "\n",
      "         ...,\n",
      "\n",
      "         [[-0.0280]],\n",
      "\n",
      "         [[ 0.1192]],\n",
      "\n",
      "         [[-0.0526]]]], size=(80, 184, 1, 1), dtype=torch.qint8,\n",
      "       quantization_scheme=torch.per_channel_affine,\n",
      "       scale=tensor([0.0021, 0.0023, 0.0029, 0.0027, 0.0023, 0.0023, 0.0023, 0.0022, 0.0020,\n",
      "        0.0022, 0.0028, 0.0018, 0.0016, 0.0013, 0.0022, 0.0025, 0.0016, 0.0013,\n",
      "        0.0018, 0.0028, 0.0033, 0.0015, 0.0029, 0.0024, 0.0018, 0.0026, 0.0018,\n",
      "        0.0016, 0.0028, 0.0022, 0.0016, 0.0028, 0.0027, 0.0029, 0.0016, 0.0030,\n",
      "        0.0027, 0.0022, 0.0028, 0.0029, 0.0022, 0.0014, 0.0031, 0.0021, 0.0016,\n",
      "        0.0020, 0.0016, 0.0028, 0.0017, 0.0023, 0.0016, 0.0016, 0.0017, 0.0022,\n",
      "        0.0025, 0.0029, 0.0031, 0.0017, 0.0024, 0.0043, 0.0017, 0.0026, 0.0042,\n",
      "        0.0033, 0.0019, 0.0026, 0.0027, 0.0032, 0.0034, 0.0041, 0.0013, 0.0029,\n",
      "        0.0027, 0.0022, 0.0032, 0.0027, 0.0019, 0.0018, 0.0016, 0.0035],\n",
      "       dtype=torch.float64),\n",
      "       zero_point=tensor([0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0,\n",
      "        0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0,\n",
      "        0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0,\n",
      "        0, 0, 0, 0, 0, 0, 0, 0]),\n",
      "       axis=0)), ('features.9.block.2.0.bias', Parameter containing:\n",
      "tensor([ 0.0858,  0.2527,  0.2151,  0.0812, -0.1845,  0.3453,  0.0200,  0.1190,\n",
      "         0.0311,  0.0912, -0.0609,  0.0450, -0.2357, -0.2110, -0.0188, -0.3663,\n",
      "         0.2331,  0.1981, -0.0376, -0.5290,  0.4514, -0.1668,  0.1621,  0.3229,\n",
      "        -0.0285, -0.1653,  0.1498,  0.0114, -0.2250,  0.3769, -0.1498, -0.1648,\n",
      "         0.1488, -0.0810, -0.3339, -0.1518, -0.2557,  0.3410,  0.0076, -0.2544,\n",
      "        -0.1752,  0.0671,  0.0713,  0.1347,  0.0545,  0.0381, -0.1089, -0.3091,\n",
      "         0.1478,  0.0985, -0.2080,  0.2423, -0.1253,  0.0683,  0.2665,  0.4221,\n",
      "         0.0644, -0.0127,  0.0972,  0.2649, -0.1577,  0.2741, -0.6193,  0.4321,\n",
      "         0.4084,  0.1442, -0.3072,  0.0394, -0.1383,  0.1271,  0.0254,  0.0075,\n",
      "         0.0427,  0.2271, -0.0262,  0.1404, -0.0386,  0.0759,  0.1419, -0.0265])), ('features.9.block.2.0.scale', tensor(0.2023)), ('features.9.block.2.0.zero_point', tensor(65)), ('features.9.skip_add.scale', tensor(0.4551)), ('features.9.skip_add.zero_point', tensor(64)), ('features.10.block.0.0.weight', tensor([[[[ 0.0600]],\n",
      "\n",
      "         [[ 0.0905]],\n",
      "\n",
      "         [[ 0.0371]],\n",
      "\n",
      "         ...,\n",
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      "         [[ 0.0524]],\n",
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      "         [[-0.0414]],\n",
      "\n",
      "         [[ 0.0273]]],\n",
      "\n",
      "\n",
      "        [[[-0.0056]],\n",
      "\n",
      "         [[ 0.0266]],\n",
      "\n",
      "         [[-0.0084]],\n",
      "\n",
      "         ...,\n",
      "\n",
      "         [[-0.0210]],\n",
      "\n",
      "         [[-0.0070]],\n",
      "\n",
      "         [[ 0.0042]]],\n",
      "\n",
      "\n",
      "        [[[ 0.0179]],\n",
      "\n",
      "         [[ 0.0179]],\n",
      "\n",
      "         [[ 0.0074]],\n",
      "\n",
      "         ...,\n",
      "\n",
      "         [[ 0.0116]],\n",
      "\n",
      "         [[ 0.0042]],\n",
      "\n",
      "         [[-0.0676]]],\n",
      "\n",
      "\n",
      "        ...,\n",
      "\n",
      "\n",
      "        [[[-0.0400]],\n",
      "\n",
      "         [[-0.0536]],\n",
      "\n",
      "         [[-0.0007]],\n",
      "\n",
      "         ...,\n",
      "\n",
      "         [[ 0.0088]],\n",
      "\n",
      "         [[ 0.0326]],\n",
      "\n",
      "         [[-0.0143]]],\n",
      "\n",
      "\n",
      "        [[[ 0.0576]],\n",
      "\n",
      "         [[ 0.0314]],\n",
      "\n",
      "         [[ 0.0503]],\n",
      "\n",
      "         ...,\n",
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      "         [[-0.1089]],\n",
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      "         [[ 0.0147]],\n",
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      "         [[-0.0702]]],\n",
      "\n",
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      "        [[[ 0.0400]],\n",
      "\n",
      "         [[-0.0109]],\n",
      "\n",
      "         [[-0.0027]],\n",
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      "         ...,\n",
      "\n",
      "         [[-0.0136]],\n",
      "\n",
      "         [[ 0.0455]],\n",
      "\n",
      "         [[-0.1001]]]], size=(184, 80, 1, 1), dtype=torch.qint8,\n",
      "       quantization_scheme=torch.per_channel_affine,\n",
      "       scale=tensor([0.0011, 0.0007, 0.0011, 0.0012, 0.0007, 0.0009, 0.0012, 0.0016, 0.0009,\n",
      "        0.0007, 0.0014, 0.0007, 0.0010, 0.0014, 0.0012, 0.0010, 0.0014, 0.0012,\n",
      "        0.0007, 0.0011, 0.0008, 0.0007, 0.0012, 0.0010, 0.0006, 0.0007, 0.0010,\n",
      "        0.0014, 0.0010, 0.0006, 0.0011, 0.0013, 0.0008, 0.0007, 0.0009, 0.0014,\n",
      "        0.0013, 0.0013, 0.0009, 0.0007, 0.0005, 0.0010, 0.0007, 0.0011, 0.0008,\n",
      "        0.0005, 0.0007, 0.0007, 0.0020, 0.0009, 0.0010, 0.0017, 0.0015, 0.0006,\n",
      "        0.0006, 0.0011, 0.0009, 0.0008, 0.0007, 0.0012, 0.0012, 0.0010, 0.0006,\n",
      "        0.0012, 0.0009, 0.0005, 0.0010, 0.0007, 0.0007, 0.0019, 0.0009, 0.0009,\n",
      "        0.0014, 0.0012, 0.0016, 0.0013, 0.0008, 0.0006, 0.0006, 0.0009, 0.0009,\n",
      "        0.0005, 0.0007, 0.0010, 0.0013, 0.0011, 0.0008, 0.0022, 0.0010, 0.0006,\n",
      "        0.0007, 0.0007, 0.0013, 0.0014, 0.0009, 0.0006, 0.0012, 0.0012, 0.0005,\n",
      "        0.0008, 0.0010, 0.0016, 0.0011, 0.0006, 0.0009, 0.0012, 0.0007, 0.0007,\n",
      "        0.0009, 0.0006, 0.0008, 0.0006, 0.0011, 0.0007, 0.0006, 0.0006, 0.0012,\n",
      "        0.0009, 0.0009, 0.0005, 0.0009, 0.0016, 0.0013, 0.0010, 0.0014, 0.0009,\n",
      "        0.0007, 0.0009, 0.0012, 0.0012, 0.0008, 0.0007, 0.0009, 0.0011, 0.0008,\n",
      "        0.0013, 0.0010, 0.0016, 0.0010, 0.0013, 0.0007, 0.0007, 0.0006, 0.0012,\n",
      "        0.0016, 0.0010, 0.0015, 0.0008, 0.0012, 0.0009, 0.0013, 0.0008, 0.0014,\n",
      "        0.0011, 0.0008, 0.0006, 0.0006, 0.0009, 0.0008, 0.0009, 0.0013, 0.0012,\n",
      "        0.0008, 0.0007, 0.0007, 0.0008, 0.0011, 0.0009, 0.0011, 0.0008, 0.0013,\n",
      "        0.0011, 0.0006, 0.0005, 0.0006, 0.0012, 0.0006, 0.0008, 0.0005, 0.0007,\n",
      "        0.0006, 0.0007, 0.0010, 0.0009], dtype=torch.float64),\n",
      "       zero_point=tensor([0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0,\n",
      "        0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0,\n",
      "        0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0,\n",
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      "        0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0,\n",
      "        0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0]),\n",
      "       axis=0)), ('features.10.block.0.0.bias', Parameter containing:\n",
      "tensor([-0.0311, -0.0310, -0.1115,  0.0656,  0.0372,  0.1212,  0.0054,  0.0373,\n",
      "        -0.0531, -0.0655, -0.0076, -0.0075, -0.0150, -0.0666,  0.0405, -0.0961,\n",
      "        -0.0520, -0.0385,  0.0798, -0.1100,  0.1115, -0.1019, -0.0570,  0.1044,\n",
      "         0.0823,  0.0229,  0.0248,  0.0279,  0.0026,  0.1008, -0.0149, -0.0393,\n",
      "        -0.0763, -0.0291, -0.0164,  0.0255,  0.0744, -0.0253,  0.1027, -0.0296,\n",
      "         0.0343,  0.0281,  0.0193, -0.0017, -0.0701,  0.0372,  0.0752,  0.0211,\n",
      "         0.0529, -0.1349,  0.0449,  0.0742,  0.1527,  0.0552,  0.0055, -0.0307,\n",
      "        -0.0581, -0.0043,  0.0290,  0.0437,  0.0169,  0.0049, -0.1061, -0.0354,\n",
      "         0.0064,  0.0840, -0.0715,  0.0211, -0.1046,  0.0110, -0.0591, -0.0224,\n",
      "         0.0231, -0.0362,  0.0706, -0.0096, -0.0411, -0.0420, -0.0332,  0.0605,\n",
      "         0.0367, -0.0525, -0.0105, -0.0437, -0.0298,  0.1002, -0.0280,  0.1498,\n",
      "         0.0509,  0.0722,  0.0905,  0.0283, -0.0658, -0.0049,  0.0203,  0.1120,\n",
      "         0.0913,  0.0540,  0.0734,  0.0822,  0.0433, -0.0500, -0.0664,  0.0362,\n",
      "        -0.1058,  0.0382, -0.0034, -0.0046,  0.0456,  0.1149,  0.1130, -0.0320,\n",
      "         0.0203,  0.0654, -0.0276, -0.0784, -0.1207, -0.0415,  0.0916, -0.0923,\n",
      "        -0.1077,  0.0330, -0.0599, -0.0315,  0.0509, -0.0367,  0.0033, -0.0013,\n",
      "         0.0560, -0.1464, -0.0626, -0.1289, -0.0691,  0.0078, -0.0682, -0.0697,\n",
      "         0.0286,  0.0246,  0.1238,  0.1008,  0.0120, -0.0930,  0.1291,  0.0292,\n",
      "        -0.0051,  0.1108,  0.0339,  0.0887,  0.0596,  0.0105,  0.0548,  0.0165,\n",
      "        -0.1523, -0.0696, -0.0996,  0.0699,  0.1414,  0.0437,  0.0579, -0.0336,\n",
      "         0.0168, -0.0533,  0.0262,  0.0666,  0.0061,  0.0460, -0.0045,  0.0007,\n",
      "        -0.0203,  0.0955,  0.0570, -0.0852,  0.0704, -0.1024,  0.0902,  0.0380,\n",
      "         0.0793,  0.0171,  0.0628, -0.0847,  0.0346,  0.0488, -0.1092, -0.0525])), ('features.10.block.0.0.scale', tensor(0.2013)), ('features.10.block.0.0.zero_point', tensor(62)), ('features.10.block.0.2.scale', tensor(0.0981)), ('features.10.block.0.2.zero_point', tensor(4)), ('features.10.block.1.0.weight', tensor([[[[-0.7072,  0.2184, -1.3312],\n",
      "          [-1.0816, -0.1560, -0.0416],\n",
      "          [ 0.1560,  0.4680,  0.1352]]],\n",
      "\n",
      "\n",
      "        [[[-1.1662, -1.9387,  0.3786],\n",
      "          [-0.8482,  0.0909,  0.3786],\n",
      "          [ 0.0000, -0.4695,  0.0303]]],\n",
      "\n",
      "\n",
      "        [[[-0.7328, -0.4830, -0.9827],\n",
      "          [-2.0652,  0.4330, -0.1499],\n",
      "          [ 0.0500,  0.9660,  1.5989]]],\n",
      "\n",
      "\n",
      "        ...,\n",
      "\n",
      "\n",
      "        [[[ 0.4460,  0.0669,  0.3791],\n",
      "          [ 0.3568,  0.1784,  0.0223],\n",
      "          [ 1.2711, -0.5352, -2.7429]]],\n",
      "\n",
      "\n",
      "        [[[ 0.0436, -0.1016, -0.3775],\n",
      "          [ 1.8440, -0.1307,  0.0290],\n",
      "          [-1.7133,  0.4501, -1.7423]]],\n",
      "\n",
      "\n",
      "        [[[-0.3506, -0.2454, -0.4382],\n",
      "          [-0.3418, -0.3944,  0.1928],\n",
      "          [-0.0526, -1.1219,  0.1578]]]], size=(184, 1, 3, 3),\n",
      "       dtype=torch.qint8, quantization_scheme=torch.per_channel_affine,\n",
      "       scale=tensor([0.0104, 0.0151, 0.0167, 0.0171, 0.0030, 0.0083, 0.0135, 0.0129, 0.0047,\n",
      "        0.0135, 0.0102, 0.0047, 0.0090, 0.0170, 0.0057, 0.0120, 0.0105, 0.0137,\n",
      "        0.0060, 0.0153, 0.0034, 0.0188, 0.0176, 0.0036, 0.0051, 0.0039, 0.0071,\n",
      "        0.0092, 0.0060, 0.0076, 0.0161, 0.0125, 0.0178, 0.0201, 0.0164, 0.0084,\n",
      "        0.0063, 0.0123, 0.0043, 0.0203, 0.0044, 0.0050, 0.0057, 0.0076, 0.0142,\n",
      "        0.0100, 0.0055, 0.0064, 0.0082, 0.0213, 0.0113, 0.0088, 0.0130, 0.0135,\n",
      "        0.0168, 0.0076, 0.0135, 0.0065, 0.0037, 0.0062, 0.0165, 0.0166, 0.0229,\n",
      "        0.0089, 0.0169, 0.0046, 0.0149, 0.0041, 0.0104, 0.0105, 0.0156, 0.0100,\n",
      "        0.0139, 0.0071, 0.0106, 0.0047, 0.0218, 0.0130, 0.0221, 0.0063, 0.0060,\n",
      "        0.0036, 0.0043, 0.0111, 0.0053, 0.0059, 0.0186, 0.0114, 0.0093, 0.0032,\n",
      "        0.0058, 0.0049, 0.0112, 0.0082, 0.0126, 0.0045, 0.0080, 0.0059, 0.0035,\n",
      "        0.0032, 0.0070, 0.0085, 0.0104, 0.0048, 0.0112, 0.0050, 0.0094, 0.0186,\n",
      "        0.0036, 0.0048, 0.0062, 0.0138, 0.0061, 0.0034, 0.0018, 0.0162, 0.0084,\n",
      "        0.0126, 0.0082, 0.0125, 0.0145, 0.0114, 0.0124, 0.0090, 0.0123, 0.0038,\n",
      "        0.0124, 0.0131, 0.0094, 0.0128, 0.0188, 0.0164, 0.0134, 0.0042, 0.0099,\n",
      "        0.0163, 0.0069, 0.0079, 0.0073, 0.0049, 0.0152, 0.0196, 0.0065, 0.0098,\n",
      "        0.0066, 0.0076, 0.0079, 0.0044, 0.0091, 0.0151, 0.0064, 0.0045, 0.0085,\n",
      "        0.0177, 0.0169, 0.0033, 0.0046, 0.0129, 0.0153, 0.0146, 0.0151, 0.0206,\n",
      "        0.0175, 0.0049, 0.0188, 0.0036, 0.0082, 0.0129, 0.0111, 0.0098, 0.0056,\n",
      "        0.0149, 0.0050, 0.0150, 0.0081, 0.0091, 0.0040, 0.0086, 0.0037, 0.0143,\n",
      "        0.0056, 0.0223, 0.0145, 0.0088], dtype=torch.float64),\n",
      "       zero_point=tensor([0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0,\n",
      "        0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0,\n",
      "        0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0,\n",
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      "        0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0,\n",
      "        0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0]),\n",
      "       axis=0)), ('features.10.block.1.0.bias', Parameter containing:\n",
      "tensor([ 0.4331,  0.4246,  0.1856, -0.0997,  0.1275,  0.2074, -0.1387,  0.0066,\n",
      "        -0.1962,  0.2647,  0.0872,  0.2297,  0.4193,  0.2101,  0.1363,  0.3818,\n",
      "         0.4058,  0.0861,  0.3193,  0.1799,  0.1447,  0.2674,  0.2171,  0.3236,\n",
      "         0.1003, -0.0705,  0.3928, -0.0518, -0.3055,  0.1141,  0.0556,  0.3105,\n",
      "        -0.2275, -0.3545, -0.1185,  0.0650,  0.0262, -0.4273,  0.1220, -0.2737,\n",
      "         0.1675, -0.1349,  0.1776, -0.1889,  0.4603, -0.0514, -0.1848, -0.1511,\n",
      "         0.2831, -0.0566,  0.2680, -0.3418, -0.0394,  0.5034,  0.1138, -0.3404,\n",
      "         0.1540, -0.1199, -0.1542, -0.1670,  0.2660, -0.1735, -0.0416,  0.3677,\n",
      "        -0.0162, -0.1197,  0.0232,  0.1170, -0.2672, -0.1680,  0.3196,  0.0206,\n",
      "        -0.2455,  0.3728, -0.1567,  0.3060, -0.2863,  0.3253,  0.2922, -0.0970,\n",
      "         0.0927,  0.0647,  0.0923,  0.2383, -0.2895, -0.3436,  0.1228, -0.0955,\n",
      "         0.0222, -0.1250, -0.1627, -0.1839,  0.0757,  0.1455, -0.0214, -0.1172,\n",
      "        -0.0172, -0.0737, -0.1040, -0.1292,  0.0738, -0.1753, -0.3073, -0.1434,\n",
      "         0.1094, -0.0316, -0.4578, -0.0396, -0.2333, -0.0849,  0.1828, -0.0614,\n",
      "         0.0756,  0.1680,  0.0967, -0.3467,  0.2918,  0.2393, -0.1726, -0.3146,\n",
      "        -0.2777, -0.0032,  0.0331, -0.1284, -0.0764,  0.0649, -0.3791, -0.0023,\n",
      "         0.3787, -0.1618, -0.0959, -0.0534,  0.0493,  0.2335,  0.1718,  0.0949,\n",
      "         0.6305, -0.1508,  0.1921,  0.2504,  0.1419,  0.2627, -0.1247,  0.1567,\n",
      "        -0.3249,  0.0507, -0.3945, -0.1540, -0.1033,  0.0883,  0.4421,  0.1205,\n",
      "         0.2436,  0.0227, -0.1367,  0.1657,  0.1940,  0.1362, -0.2476,  0.0566,\n",
      "        -0.3726,  0.0507,  0.0280,  0.1283, -0.1638, -0.0940,  0.1816, -0.1454,\n",
      "        -0.2732, -0.1234,  0.0559, -0.0586, -0.1250,  0.3664,  0.0926,  0.0965,\n",
      "         0.2188,  0.7004,  0.1306,  0.0096, -0.0870,  0.0988,  0.1763,  0.3627])), ('features.10.block.1.0.scale', tensor(0.2294)), ('features.10.block.1.0.zero_point', tensor(62)), ('features.10.block.1.2.scale', tensor(0.1161)), ('features.10.block.1.2.zero_point', tensor(3)), ('features.10.block.2.0.weight', tensor([[[[ 0.0287]],\n",
      "\n",
      "         [[-0.0197]],\n",
      "\n",
      "         [[ 0.1380]],\n",
      "\n",
      "         ...,\n",
      "\n",
      "         [[-0.0610]],\n",
      "\n",
      "         [[ 0.1524]],\n",
      "\n",
      "         [[-0.0305]]],\n",
      "\n",
      "\n",
      "        [[[ 0.1463]],\n",
      "\n",
      "         [[ 0.0078]],\n",
      "\n",
      "         [[ 0.1541]],\n",
      "\n",
      "         ...,\n",
      "\n",
      "         [[-0.0078]],\n",
      "\n",
      "         [[-0.0157]],\n",
      "\n",
      "         [[-0.0183]]],\n",
      "\n",
      "\n",
      "        [[[ 0.0431]],\n",
      "\n",
      "         [[ 0.0305]],\n",
      "\n",
      "         [[ 0.0305]],\n",
      "\n",
      "         ...,\n",
      "\n",
      "         [[ 0.1112]],\n",
      "\n",
      "         [[ 0.0735]],\n",
      "\n",
      "         [[ 0.0197]]],\n",
      "\n",
      "\n",
      "        ...,\n",
      "\n",
      "\n",
      "        [[[-0.0948]],\n",
      "\n",
      "         [[-0.1214]],\n",
      "\n",
      "         [[ 0.1214]],\n",
      "\n",
      "         ...,\n",
      "\n",
      "         [[-0.0118]],\n",
      "\n",
      "         [[-0.1925]],\n",
      "\n",
      "         [[ 0.0237]]],\n",
      "\n",
      "\n",
      "        [[[ 0.0247]],\n",
      "\n",
      "         [[-0.0725]],\n",
      "\n",
      "         [[ 0.0123]],\n",
      "\n",
      "         ...,\n",
      "\n",
      "         [[-0.0540]],\n",
      "\n",
      "         [[-0.1311]],\n",
      "\n",
      "         [[ 0.0540]]],\n",
      "\n",
      "\n",
      "        [[[ 0.0199]],\n",
      "\n",
      "         [[-0.1653]],\n",
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      "         [[-0.0020]],\n",
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      "         [[-0.1076]]]], size=(80, 184, 1, 1), dtype=torch.qint8,\n",
      "       quantization_scheme=torch.per_channel_affine,\n",
      "       scale=tensor([0.0018, 0.0026, 0.0018, 0.0016, 0.0023, 0.0019, 0.0034, 0.0017, 0.0017,\n",
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      "        0.0032, 0.0019, 0.0018, 0.0026, 0.0029, 0.0030, 0.0015, 0.0020],\n",
      "       dtype=torch.float64),\n",
      "       zero_point=tensor([0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0,\n",
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      "        0, 0, 0, 0, 0, 0, 0, 0]),\n",
      "       axis=0)), ('features.10.block.2.0.bias', Parameter containing:\n",
      "tensor([-0.2911,  0.2304,  0.0579,  0.3724,  0.3291,  0.0739,  0.2129,  0.0820,\n",
      "         0.1228,  0.0940, -0.2314, -0.2002, -0.0682, -0.0097,  0.2054,  0.0469,\n",
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      "        -0.0005,  0.2288, -0.0668,  0.0707, -0.2602, -0.1184, -0.0308,  0.0863])), ('features.10.block.2.0.scale', tensor(0.1948)), ('features.10.block.2.0.zero_point', tensor(63)), ('features.10.skip_add.scale', tensor(0.5530)), ('features.10.skip_add.zero_point', tensor(65)), ('features.11.block.0.0.weight', tensor([[[[-0.0044]],\n",
      "\n",
      "         [[ 0.0149]],\n",
      "\n",
      "         [[ 0.0066]],\n",
      "\n",
      "         ...,\n",
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      "       quantization_scheme=torch.per_channel_affine,\n",
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      "       axis=0)), ('features.11.block.0.0.bias', Parameter containing:\n",
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      "        -0.0014,  0.0588,  0.0722, -0.0988, -0.0147,  0.0305, -0.0788, -0.0912,\n",
      "        -0.0451, -0.0154,  0.0880,  0.0561,  0.0482,  0.0482, -0.1460, -0.0289,\n",
      "        -0.0668,  0.0052, -0.0582,  0.0294,  0.0422, -0.0957,  0.0341,  0.0631,\n",
      "        -0.0637,  0.0276, -0.0083,  0.0164, -0.0717,  0.1155,  0.1317,  0.1126,\n",
      "        -0.0991, -0.0813,  0.0825,  0.0773,  0.0346, -0.0567, -0.0654, -0.0324,\n",
      "         0.0404,  0.0480,  0.0141,  0.0992,  0.0733, -0.0681,  0.0873,  0.0952,\n",
      "         0.0786,  0.0680,  0.1099,  0.0107,  0.0984, -0.0051,  0.0154, -0.0281,\n",
      "         0.0435,  0.0313,  0.1074, -0.0497, -0.0297,  0.0963,  0.1198, -0.0562,\n",
      "        -0.1592,  0.0555,  0.0382,  0.0215, -0.0167,  0.0929, -0.0838,  0.0611,\n",
      "         0.0501,  0.0257,  0.0328,  0.0989,  0.1293,  0.0297,  0.0642,  0.0279,\n",
      "         0.0104,  0.0804, -0.0778, -0.1301,  0.0358, -0.0039, -0.0185, -0.0981,\n",
      "        -0.1079, -0.0728, -0.0399, -0.0658, -0.0748,  0.1021, -0.0922,  0.0815,\n",
      "        -0.0093,  0.0301,  0.0089, -0.1997, -0.1316, -0.1092,  0.0394, -0.0618,\n",
      "         0.0420, -0.0311,  0.0832,  0.0463,  0.0465,  0.0881, -0.0568,  0.0518,\n",
      "        -0.0787, -0.0556, -0.0131,  0.0476,  0.0108,  0.0859, -0.0049, -0.0220])), ('features.11.block.0.0.scale', tensor(0.2051)), ('features.11.block.0.0.zero_point', tensor(61)), ('features.11.block.0.2.scale', tensor(0.1026)), ('features.11.block.0.2.zero_point', tensor(4)), ('features.11.block.1.0.weight', tensor([[[[ 0.7046,  0.0931, -0.0133],\n",
      "          [ 0.1064,  1.0503,  0.4653],\n",
      "          [ 0.5052, -1.1965,  1.6884]]],\n",
      "\n",
      "\n",
      "        [[[ 0.6834, -2.6506,  0.7869],\n",
      "          [ 1.3460,  1.6566, -1.0147],\n",
      "          [-0.5384,  1.7394, -0.4142]]],\n",
      "\n",
      "\n",
      "        [[[-0.8085,  0.5615,  0.6962],\n",
      "          [-2.2908, -0.8759,  2.7400],\n",
      "          [ 0.5390, -0.7861, -0.6738]]],\n",
      "\n",
      "\n",
      "        ...,\n",
      "\n",
      "\n",
      "        [[[-0.1200, -0.5401, -0.5909],\n",
      "          [ 0.3555, -0.2262, -0.1754],\n",
      "          [-0.0277,  0.0508, -0.0692]]],\n",
      "\n",
      "\n",
      "        [[[ 0.2668,  0.2177, -0.7303],\n",
      "          [ 0.0843, -0.4143, -0.5969],\n",
      "          [-0.1475,  0.8567,  0.5477]]],\n",
      "\n",
      "\n",
      "        [[[-0.4489, -0.1795,  0.0539],\n",
      "          [-0.2514, -2.2981,  0.1616],\n",
      "          [-0.2334, -0.4129,  1.2029]]]], size=(480, 1, 3, 3),\n",
      "       dtype=torch.qint8, quantization_scheme=torch.per_channel_affine,\n",
      "       scale=tensor([0.0133, 0.0207, 0.0225, 0.0045, 0.0054, 0.0024, 0.0141, 0.0026, 0.0093,\n",
      "        0.0074, 0.0053, 0.0128, 0.0032, 0.0026, 0.0103, 0.0059, 0.0054, 0.0029,\n",
      "        0.0092, 0.0064, 0.0048, 0.0048, 0.0105, 0.0072, 0.0035, 0.0041, 0.0056,\n",
      "        0.0044, 0.0166, 0.0059, 0.0278, 0.0058, 0.0052, 0.0177, 0.0043, 0.0110,\n",
      "        0.0074, 0.0068, 0.0067, 0.0083, 0.0111, 0.0203, 0.0127, 0.0047, 0.0141,\n",
      "        0.0058, 0.0151, 0.0033, 0.0033, 0.0049, 0.0056, 0.0052, 0.0071, 0.0116,\n",
      "        0.0059, 0.0049, 0.0135, 0.0064, 0.0098, 0.0116, 0.0082, 0.0093, 0.0079,\n",
      "        0.0031, 0.0117, 0.0080, 0.0066, 0.0181, 0.0041, 0.0045, 0.0057, 0.0086,\n",
      "        0.0052, 0.0143, 0.0159, 0.0139, 0.0060, 0.0079, 0.0097, 0.0065, 0.0050,\n",
      "        0.0158, 0.0027, 0.0104, 0.0030, 0.0174, 0.0086, 0.0113, 0.0049, 0.0124,\n",
      "        0.0088, 0.0078, 0.0050, 0.0196, 0.0042, 0.0114, 0.0040, 0.0194, 0.0101,\n",
      "        0.0056, 0.0033, 0.0058, 0.0138, 0.0029, 0.0096, 0.0101, 0.0036, 0.0114,\n",
      "        0.0032, 0.0128, 0.0046, 0.0191, 0.0133, 0.0043, 0.0034, 0.0088, 0.0037,\n",
      "        0.0027, 0.0131, 0.0116, 0.0052, 0.0104, 0.0088, 0.0096, 0.0072, 0.0144,\n",
      "        0.0041, 0.0108, 0.0061, 0.0032, 0.0041, 0.0038, 0.0048, 0.0111, 0.0151,\n",
      "        0.0111, 0.0134, 0.0112, 0.0068, 0.0076, 0.0041, 0.0080, 0.0047, 0.0161,\n",
      "        0.0049, 0.0091, 0.0065, 0.0115, 0.0033, 0.0043, 0.0053, 0.0053, 0.0025,\n",
      "        0.0080, 0.0065, 0.0122, 0.0046, 0.0041, 0.0085, 0.0198, 0.0039, 0.0035,\n",
      "        0.0123, 0.0046, 0.0136, 0.0078, 0.0042, 0.0056, 0.0067, 0.0173, 0.0102,\n",
      "        0.0083, 0.0159, 0.0044, 0.0243, 0.0036, 0.0034, 0.0069, 0.0034, 0.0052,\n",
      "        0.0204, 0.0154, 0.0095, 0.0178, 0.0031, 0.0029, 0.0059, 0.0054, 0.0056,\n",
      "        0.0108, 0.0225, 0.0032, 0.0307, 0.0044, 0.0111, 0.0113, 0.0036, 0.0077,\n",
      "        0.0109, 0.0084, 0.0064, 0.0042, 0.0113, 0.0177, 0.0033, 0.0028, 0.0076,\n",
      "        0.0120, 0.0162, 0.0045, 0.0041, 0.0037, 0.0183, 0.0160, 0.0123, 0.0066,\n",
      "        0.0157, 0.0048, 0.0218, 0.0098, 0.0041, 0.0020, 0.0047, 0.0165, 0.0071,\n",
      "        0.0201, 0.0177, 0.0056, 0.0070, 0.0083, 0.0187, 0.0055, 0.0022, 0.0076,\n",
      "        0.0165, 0.0139, 0.0042, 0.0049, 0.0056, 0.0143, 0.0074, 0.0121, 0.0103,\n",
      "        0.0041, 0.0097, 0.0029, 0.0115, 0.0050, 0.0182, 0.0109, 0.0120, 0.0108,\n",
      "        0.0178, 0.0172, 0.0039, 0.0069, 0.0031, 0.0053, 0.0102, 0.0038, 0.0055,\n",
      "        0.0072, 0.0109, 0.0053, 0.0030, 0.0041, 0.0078, 0.0161, 0.0124, 0.0144,\n",
      "        0.0110, 0.0102, 0.0125, 0.0177, 0.0054, 0.0052, 0.0061, 0.0085, 0.0074,\n",
      "        0.0062, 0.0117, 0.0069, 0.0073, 0.0030, 0.0070, 0.0036, 0.0072, 0.0116,\n",
      "        0.0161, 0.0030, 0.0087, 0.0076, 0.0053, 0.0083, 0.0138, 0.0125, 0.0197,\n",
      "        0.0110, 0.0042, 0.0028, 0.0204, 0.0086, 0.0062, 0.0035, 0.0033, 0.0080,\n",
      "        0.0102, 0.0034, 0.0040, 0.0115, 0.0079, 0.0075, 0.0034, 0.0097, 0.0052,\n",
      "        0.0101, 0.0052, 0.0069, 0.0034, 0.0066, 0.0078, 0.0207, 0.0091, 0.0033,\n",
      "        0.0058, 0.0131, 0.0092, 0.0093, 0.0078, 0.0070, 0.0113, 0.0066, 0.0078,\n",
      "        0.0137, 0.0026, 0.0113, 0.0028, 0.0044, 0.0049, 0.0163, 0.0092, 0.0062,\n",
      "        0.0083, 0.0059, 0.0118, 0.0038, 0.0034, 0.0082, 0.0046, 0.0119, 0.0092,\n",
      "        0.0040, 0.0142, 0.0088, 0.0034, 0.0183, 0.0110, 0.0099, 0.0121, 0.0094,\n",
      "        0.0188, 0.0031, 0.0048, 0.0062, 0.0217, 0.0071, 0.0102, 0.0108, 0.0118,\n",
      "        0.0072, 0.0038, 0.0110, 0.0060, 0.0032, 0.0162, 0.0098, 0.0096, 0.0104,\n",
      "        0.0220, 0.0134, 0.0065, 0.0172, 0.0069, 0.0066, 0.0109, 0.0073, 0.0074,\n",
      "        0.0163, 0.0134, 0.0061, 0.0045, 0.0020, 0.0152, 0.0181, 0.0043, 0.0051,\n",
      "        0.0053, 0.0114, 0.0127, 0.0073, 0.0088, 0.0049, 0.0084, 0.0039, 0.0053,\n",
      "        0.0112, 0.0039, 0.0066, 0.0022, 0.0055, 0.0046, 0.0054, 0.0025, 0.0046,\n",
      "        0.0041, 0.0048, 0.0045, 0.0033, 0.0039, 0.0132, 0.0122, 0.0026, 0.0034,\n",
      "        0.0139, 0.0092, 0.0055, 0.0028, 0.0156, 0.0122, 0.0042, 0.0135, 0.0081,\n",
      "        0.0081, 0.0061, 0.0065, 0.0053, 0.0036, 0.0063, 0.0104, 0.0064, 0.0060,\n",
      "        0.0037, 0.0115, 0.0186, 0.0049, 0.0064, 0.0070, 0.0180, 0.0106, 0.0091,\n",
      "        0.0157, 0.0059, 0.0151, 0.0078, 0.0088, 0.0139, 0.0210, 0.0067, 0.0037,\n",
      "        0.0118, 0.0067, 0.0181, 0.0058, 0.0134, 0.0034, 0.0099, 0.0041, 0.0147,\n",
      "        0.0044, 0.0086, 0.0102, 0.0055, 0.0199, 0.0103, 0.0059, 0.0106, 0.0038,\n",
      "        0.0046, 0.0070, 0.0180], dtype=torch.float64),\n",
      "       zero_point=tensor([0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0,\n",
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      "       axis=0)), ('features.11.block.1.0.bias', Parameter containing:\n",
      "tensor([-3.9727e-01, -2.6811e-01, -7.4595e-03,  2.3431e-01, -3.7819e-01,\n",
      "        -1.4833e-01, -1.8430e-01, -1.7534e-01,  3.3369e-01, -1.7420e-01,\n",
      "         2.8060e-01,  5.0963e-01,  2.3574e-01,  1.7179e-01,  4.5923e-01,\n",
      "         1.9289e-01,  7.7697e-02,  2.2963e-01, -3.7666e-01,  5.1967e-01,\n",
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      "         9.2026e-02, -5.0063e-01,  2.1383e-01,  3.3389e-01,  6.2365e-01,\n",
      "         2.7360e-01,  1.2944e-01, -3.5807e-02, -3.1570e-01,  4.6875e-01,\n",
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      "        -4.0136e-02, -3.2111e-01,  8.0131e-03, -1.5478e-01, -1.5714e-01,\n",
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      "         3.0911e-01,  9.7475e-02, -1.2713e-01,  2.1570e-01,  2.8933e-01,\n",
      "        -7.2061e-02, -1.0257e-01,  2.2732e-01, -3.6782e-01,  1.8406e-01,\n",
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      "        -3.4191e-01,  1.0897e-01,  1.5422e-01, -3.3977e-01, -1.4273e-01,\n",
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      "         1.3200e-01,  1.8635e-01,  3.4880e-01,  1.7813e-01, -2.0099e-01,\n",
      "         2.6605e-01, -8.6852e-02, -3.3762e-01,  2.2496e-01,  2.0965e-01,\n",
      "         5.7585e-02,  1.6073e-01, -1.1792e-01,  2.6978e-01,  6.5418e-01,\n",
      "        -2.4972e-01, -1.0163e-01, -8.7886e-02,  2.3965e-01,  8.2883e-03,\n",
      "        -1.6150e-01,  4.0730e-01,  6.3263e-01,  1.5312e-01,  3.3182e-01,\n",
      "         8.6467e-02, -1.8114e-01, -1.5219e-01,  3.4267e-02, -1.7050e-01,\n",
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      "         1.9485e-01,  2.4175e-01, -1.3750e-01, -6.7912e-02,  1.7349e-02,\n",
      "         5.1624e-01, -5.1976e-02, -1.3738e-01, -6.8072e-02, -2.1481e-01,\n",
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      "        -2.8064e-01,  2.1712e-01,  3.3801e-02,  8.5015e-02,  3.6138e-01,\n",
      "         4.5825e-01, -3.8939e-01, -1.3956e-01, -1.9484e-01,  5.0059e-01,\n",
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      "        -1.8613e-01,  8.5385e-02,  2.7293e-01,  4.7002e-01, -1.2407e-01,\n",
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      "        -1.6662e-01,  4.7122e-01,  3.1183e-02,  7.7878e-02,  8.6156e-02,\n",
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      "         7.6281e-02, -1.6968e-01,  1.8123e-01, -8.7306e-02,  1.1769e-01,\n",
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      "         4.1344e-01,  4.6241e-01, -4.6551e-02,  4.3830e-01, -2.5282e-01,\n",
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      "        -6.4294e-02,  2.0127e-01,  3.3260e-01,  2.0161e-01, -1.8944e-01,\n",
      "         1.2265e-01,  3.8001e-01,  4.3170e-01,  2.7041e-02,  1.9963e-01,\n",
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      "         9.3407e-02, -1.7254e-01, -3.2478e-02, -3.4696e-01, -1.9738e-01,\n",
      "         5.7607e-01,  5.9799e-02, -2.3904e-02, -5.4636e-02,  4.2123e-01,\n",
      "        -2.8969e-01,  7.8658e-02, -1.8312e-02, -1.2979e-01, -1.2904e-01,\n",
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      "         3.3691e-01,  2.8845e-02,  1.2935e-01,  5.0182e-02, -5.1358e-02,\n",
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      "        -5.0636e-02,  1.2264e-01,  2.2892e-01, -4.0144e-03,  3.7449e-01])), ('features.11.block.1.0.scale', tensor(0.2365)), ('features.11.block.1.0.zero_point', tensor(64)), ('features.11.block.1.2.scale', tensor(0.1158)), ('features.11.block.1.2.zero_point', tensor(3)), ('features.11.block.2.fc1.weight', tensor([[[[-0.2441]],\n",
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      "         [[ 0.0201]],\n",
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      "       axis=0)), ('features.11.block.2.fc1.bias', Parameter containing:\n",
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      "       requires_grad=True)), ('features.11.block.2.fc2.scale', tensor(0.2445)), ('features.11.block.2.fc2.zero_point', tensor(64)), ('features.11.block.2.skip_mul.scale', tensor(0.1111)), ('features.11.block.2.skip_mul.zero_point', tensor(3)), ('features.11.block.3.0.weight', tensor([[[[-0.0142]],\n",
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      "       axis=0)), ('features.11.block.3.0.bias', Parameter containing:\n",
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      "         1.0251e-01,  1.8964e-01])), ('features.11.block.3.0.scale', tensor(0.2059)), ('features.11.block.3.0.zero_point', tensor(64)), ('features.11.skip_add.scale', tensor(1.)), ('features.11.skip_add.zero_point', tensor(0)), ('features.12.block.0.0.weight', tensor([[[[-0.0931]],\n",
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      "       quantization_scheme=torch.per_channel_affine,\n",
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      "       axis=0)), ('features.12.block.0.0.bias', Parameter containing:\n",
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      "        -0.0241,  0.0204,  0.0351, -0.0132,  0.0606, -0.0004,  0.0179,  0.0474,\n",
      "        -0.0513, -0.0183,  0.0157,  0.0550, -0.0624, -0.0440, -0.0332, -0.1106,\n",
      "        -0.0340, -0.1000, -0.0823,  0.0248, -0.0132, -0.1011,  0.0593, -0.0369,\n",
      "        -0.0140, -0.0057, -0.0110, -0.1171, -0.1054, -0.1061,  0.0606, -0.0856,\n",
      "        -0.0558, -0.0120,  0.0187, -0.0298, -0.0577, -0.0521,  0.1087,  0.0049,\n",
      "         0.0396,  0.0095, -0.0232, -0.0271,  0.0005, -0.0989, -0.0409,  0.0900,\n",
      "        -0.0299,  0.0446, -0.0339, -0.0050, -0.0817,  0.0113,  0.0477, -0.1359,\n",
      "        -0.0386, -0.1040, -0.0343, -0.0320,  0.0573, -0.1204,  0.0035,  0.0647,\n",
      "        -0.0080, -0.0467,  0.0517,  0.0138, -0.0834, -0.0731, -0.0600, -0.1129,\n",
      "        -0.0511,  0.0059,  0.0078,  0.0067, -0.1805, -0.0988, -0.0479, -0.0925,\n",
      "         0.0453,  0.0390,  0.0486, -0.1290, -0.0136,  0.0663,  0.0768,  0.0417,\n",
      "         0.0070,  0.0145, -0.0770, -0.0725,  0.0428,  0.1061,  0.0772,  0.0172,\n",
      "         0.0256, -0.0333, -0.0665, -0.0319,  0.0985, -0.0284, -0.0286, -0.0283,\n",
      "        -0.0041, -0.0052, -0.0027,  0.0103, -0.1140,  0.0350, -0.0432,  0.0608,\n",
      "        -0.0132,  0.0158,  0.0456,  0.0283, -0.0650, -0.0118, -0.1123,  0.0818,\n",
      "         0.0041,  0.0691, -0.0589,  0.0621,  0.0021, -0.0636,  0.0440,  0.0137,\n",
      "         0.0145, -0.0956,  0.0777,  0.0729,  0.0444,  0.0067,  0.0637,  0.0840,\n",
      "        -0.0254,  0.0603, -0.0319, -0.0136, -0.0653, -0.0327, -0.1111, -0.1247,\n",
      "        -0.0619, -0.0297, -0.0867,  0.0138,  0.0634, -0.0873, -0.1331,  0.0455,\n",
      "        -0.0667,  0.0338, -0.0384, -0.0509,  0.0186,  0.0428, -0.1146,  0.0342,\n",
      "        -0.0083,  0.0017,  0.0442,  0.0683,  0.0200, -0.0506,  0.0036,  0.0342,\n",
      "         0.0091,  0.0664, -0.0533,  0.0332, -0.0954,  0.0054, -0.1080, -0.0134,\n",
      "         0.0683,  0.0461, -0.0702, -0.0437, -0.0228, -0.1030,  0.0121, -0.0886,\n",
      "         0.0568, -0.0665, -0.1122, -0.0117,  0.0028, -0.0306,  0.0758,  0.0899,\n",
      "         0.0122, -0.1265,  0.0143, -0.0590,  0.0467, -0.0316, -0.0255,  0.0067,\n",
      "         0.0135, -0.0749, -0.1480, -0.0540, -0.0541, -0.0852,  0.0019, -0.0603,\n",
      "         0.0187, -0.0440, -0.0113,  0.0092,  0.0422,  0.0085,  0.0432, -0.0732,\n",
      "         0.0157,  0.0184, -0.0791,  0.0496,  0.0549,  0.0719, -0.0296,  0.0219,\n",
      "        -0.0086,  0.0026, -0.0644,  0.0962, -0.0004, -0.0031, -0.0698,  0.0315,\n",
      "         0.0856, -0.0603,  0.0999, -0.0579, -0.0004, -0.0023,  0.0177,  0.0320,\n",
      "        -0.0149,  0.0046,  0.0509,  0.0313,  0.0092,  0.0508, -0.0188, -0.0789,\n",
      "         0.0195,  0.0512, -0.1020, -0.0162, -0.0342, -0.0845,  0.0633,  0.0417,\n",
      "        -0.0293, -0.0792,  0.0208, -0.0061, -0.1460,  0.0412, -0.0232, -0.0487,\n",
      "        -0.0187, -0.0008,  0.0690, -0.0432,  0.0251,  0.0369,  0.0058, -0.0632,\n",
      "         0.0034, -0.0701,  0.0293, -0.0454,  0.0078,  0.0134, -0.0192, -0.0490,\n",
      "         0.0295, -0.0116, -0.0283,  0.0485, -0.0684, -0.0233,  0.0322,  0.0892,\n",
      "         0.1037,  0.0493, -0.0440, -0.0596, -0.0077, -0.0518, -0.0661, -0.0739,\n",
      "        -0.0893,  0.0052,  0.0010, -0.0027,  0.0135, -0.0128, -0.0055,  0.0773,\n",
      "         0.0546, -0.1726, -0.0463, -0.0515, -0.0324,  0.0780, -0.0125, -0.0450,\n",
      "        -0.0649,  0.0259, -0.0499, -0.0023, -0.1118,  0.0043,  0.0908, -0.1132,\n",
      "        -0.0116,  0.0490,  0.0079, -0.0038,  0.0110, -0.0193, -0.0303, -0.0145,\n",
      "         0.0541,  0.0163, -0.0373,  0.0810,  0.0492,  0.0572,  0.0724,  0.0575,\n",
      "         0.0456,  0.0388, -0.0329,  0.0081,  0.0221, -0.0969,  0.0240,  0.0666,\n",
      "         0.0090, -0.0988,  0.0632, -0.0469,  0.0252,  0.0532,  0.0139,  0.0108,\n",
      "        -0.0040,  0.0013, -0.0585, -0.0292,  0.0226, -0.0217, -0.0093,  0.0424,\n",
      "         0.0608, -0.0200,  0.0126,  0.0202, -0.0476, -0.0379,  0.1364,  0.0561,\n",
      "        -0.0010,  0.0195, -0.0656, -0.0337, -0.0434,  0.0111, -0.0174, -0.0633,\n",
      "        -0.0438, -0.0105, -0.1007, -0.0888, -0.0358, -0.0562,  0.0380,  0.0325,\n",
      "        -0.0755,  0.0014,  0.0508,  0.0191,  0.0115, -0.0702, -0.0902, -0.0185,\n",
      "         0.0244, -0.0847,  0.0646,  0.0101,  0.0410, -0.0079,  0.0289,  0.0493,\n",
      "         0.0271,  0.0375,  0.0259, -0.1369,  0.0746, -0.0283, -0.0575, -0.0419,\n",
      "        -0.0559,  0.0140, -0.0941, -0.0577, -0.0901,  0.0099,  0.0327, -0.0693,\n",
      "         0.0310, -0.0003,  0.0270, -0.0515,  0.0858,  0.0183,  0.0363, -0.0275,\n",
      "        -0.0512,  0.0594,  0.0366,  0.0232, -0.0151,  0.0012, -0.0276,  0.0339,\n",
      "        -0.0010, -0.0279, -0.0029,  0.0186, -0.0519,  0.0309, -0.0527,  0.0505,\n",
      "        -0.0132, -0.0463,  0.0168, -0.0429, -0.0681, -0.0326,  0.0625, -0.1054,\n",
      "        -0.1003, -0.1359, -0.0077,  0.0433, -0.1082, -0.1548, -0.0370,  0.1285,\n",
      "         0.0136, -0.0253, -0.0875,  0.0764, -0.0999, -0.1117, -0.0811, -0.1711,\n",
      "        -0.0518,  0.0477, -0.0478, -0.0320, -0.0335,  0.0366,  0.0399,  0.0359,\n",
      "         0.0081,  0.0549, -0.0039, -0.1301,  0.0485,  0.0730, -0.0911, -0.0517,\n",
      "         0.0237, -0.0113, -0.1128,  0.0677,  0.0201,  0.0320,  0.0778, -0.0760,\n",
      "        -0.0265, -0.0230, -0.0871, -0.0519,  0.0004, -0.0485, -0.0371, -0.0774])), ('features.12.block.0.0.scale', tensor(0.2089)), ('features.12.block.0.0.zero_point', tensor(62)), ('features.12.block.0.2.scale', tensor(0.1027)), ('features.12.block.0.2.zero_point', tensor(4)), ('features.12.block.1.0.weight', tensor([[[[ 0.0199,  0.3191,  0.1157],\n",
      "          [ 0.0479,  0.2992,  0.5066],\n",
      "          [ 0.0598, -0.0239,  0.0120]]],\n",
      "\n",
      "\n",
      "        [[[-0.3904, -0.1403, -0.1037],\n",
      "          [-0.0915, -0.1342, -0.0518],\n",
      "          [-0.0671, -0.0244, -0.2043]]],\n",
      "\n",
      "\n",
      "        [[[ 0.0984,  0.6946,  0.6946],\n",
      "          [-0.2297,  0.1313, -0.1969],\n",
      "          [-0.3172, -0.5852, -0.1586]]],\n",
      "\n",
      "\n",
      "        ...,\n",
      "\n",
      "\n",
      "        [[[-0.1232, -0.0843,  0.0713],\n",
      "          [-0.1395, -0.1362,  0.0259],\n",
      "          [-0.1557, -0.4151, -0.1913]]],\n",
      "\n",
      "\n",
      "        [[[ 0.2979, -0.8868,  0.3256],\n",
      "          [-0.0208, -0.3464, -0.8245],\n",
      "          [ 0.1801,  0.2425,  0.2009]]],\n",
      "\n",
      "\n",
      "        [[[ 1.0311,  0.0878,  0.2852],\n",
      "          [ 1.3931, -0.1645, -0.2633],\n",
      "          [-1.1408, -1.0201, -0.6691]]]], size=(672, 1, 3, 3),\n",
      "       dtype=torch.qint8, quantization_scheme=torch.per_channel_affine,\n",
      "       scale=tensor([0.0040, 0.0030, 0.0055, 0.0022, 0.0046, 0.0082, 0.0107, 0.0122, 0.0138,\n",
      "        0.0048, 0.0082, 0.0069, 0.0097, 0.0067, 0.0052, 0.0019, 0.0041, 0.0038,\n",
      "        0.0126, 0.0190, 0.0046, 0.0049, 0.0029, 0.0077, 0.0067, 0.0215, 0.0076,\n",
      "        0.0067, 0.0029, 0.0051, 0.0093, 0.0106, 0.0046, 0.0102, 0.0174, 0.0060,\n",
      "        0.0052, 0.0093, 0.0061, 0.0082, 0.0041, 0.0089, 0.0065, 0.0028, 0.0091,\n",
      "        0.0074, 0.0218, 0.0081, 0.0036, 0.0037, 0.0036, 0.0048, 0.0097, 0.0034,\n",
      "        0.0072, 0.0073, 0.0076, 0.0062, 0.0061, 0.0109, 0.0084, 0.0195, 0.0049,\n",
      "        0.0096, 0.0088, 0.0145, 0.0056, 0.0172, 0.0060, 0.0117, 0.0120, 0.0102,\n",
      "        0.0037, 0.0048, 0.0071, 0.0032, 0.0061, 0.0100, 0.0128, 0.0059, 0.0027,\n",
      "        0.0044, 0.0082, 0.0141, 0.0081, 0.0034, 0.0095, 0.0063, 0.0127, 0.0033,\n",
      "        0.0061, 0.0043, 0.0085, 0.0032, 0.0104, 0.0044, 0.0194, 0.0041, 0.0060,\n",
      "        0.0072, 0.0084, 0.0213, 0.0080, 0.0074, 0.0076, 0.0080, 0.0049, 0.0045,\n",
      "        0.0044, 0.0105, 0.0063, 0.0043, 0.0030, 0.0068, 0.0036, 0.0114, 0.0024,\n",
      "        0.0115, 0.0122, 0.0047, 0.0097, 0.0027, 0.0108, 0.0038, 0.0111, 0.0051,\n",
      "        0.0125, 0.0148, 0.0080, 0.0036, 0.0058, 0.0150, 0.0072, 0.0072, 0.0187,\n",
      "        0.0098, 0.0031, 0.0058, 0.0107, 0.0080, 0.0078, 0.0061, 0.0067, 0.0076,\n",
      "        0.0065, 0.0085, 0.0065, 0.0132, 0.0063, 0.0130, 0.0072, 0.0153, 0.0105,\n",
      "        0.0071, 0.0087, 0.0071, 0.0042, 0.0080, 0.0042, 0.0060, 0.0103, 0.0136,\n",
      "        0.0061, 0.0096, 0.0110, 0.0055, 0.0059, 0.0041, 0.0028, 0.0148, 0.0220,\n",
      "        0.0128, 0.0033, 0.0057, 0.0220, 0.0042, 0.0063, 0.0045, 0.0033, 0.0071,\n",
      "        0.0069, 0.0202, 0.0108, 0.0042, 0.0102, 0.0107, 0.0064, 0.0092, 0.0067,\n",
      "        0.0077, 0.0185, 0.0055, 0.0058, 0.0115, 0.0216, 0.0053, 0.0147, 0.0141,\n",
      "        0.0050, 0.0085, 0.0050, 0.0042, 0.0083, 0.0164, 0.0252, 0.0197, 0.0044,\n",
      "        0.0089, 0.0061, 0.0046, 0.0104, 0.0055, 0.0071, 0.0050, 0.0108, 0.0052,\n",
      "        0.0076, 0.0070, 0.0074, 0.0121, 0.0081, 0.0120, 0.0125, 0.0099, 0.0140,\n",
      "        0.0073, 0.0114, 0.0167, 0.0069, 0.0060, 0.0061, 0.0232, 0.0054, 0.0098,\n",
      "        0.0103, 0.0035, 0.0108, 0.0122, 0.0031, 0.0060, 0.0043, 0.0095, 0.0050,\n",
      "        0.0094, 0.0207, 0.0121, 0.0143, 0.0124, 0.0063, 0.0071, 0.0103, 0.0064,\n",
      "        0.0128, 0.0171, 0.0130, 0.0132, 0.0054, 0.0074, 0.0123, 0.0154, 0.0054,\n",
      "        0.0047, 0.0049, 0.0039, 0.0042, 0.0027, 0.0163, 0.0157, 0.0034, 0.0045,\n",
      "        0.0059, 0.0070, 0.0092, 0.0079, 0.0069, 0.0049, 0.0076, 0.0065, 0.0157,\n",
      "        0.0073, 0.0082, 0.0046, 0.0061, 0.0054, 0.0210, 0.0045, 0.0140, 0.0038,\n",
      "        0.0077, 0.0113, 0.0028, 0.0076, 0.0086, 0.0105, 0.0122, 0.0094, 0.0049,\n",
      "        0.0046, 0.0120, 0.0097, 0.0036, 0.0043, 0.0069, 0.0058, 0.0056, 0.0130,\n",
      "        0.0057, 0.0037, 0.0069, 0.0089, 0.0024, 0.0082, 0.0128, 0.0152, 0.0097,\n",
      "        0.0141, 0.0075, 0.0090, 0.0057, 0.0121, 0.0076, 0.0079, 0.0141, 0.0054,\n",
      "        0.0029, 0.0119, 0.0145, 0.0068, 0.0038, 0.0025, 0.0057, 0.0119, 0.0105,\n",
      "        0.0049, 0.0068, 0.0097, 0.0056, 0.0062, 0.0061, 0.0063, 0.0034, 0.0081,\n",
      "        0.0099, 0.0085, 0.0053, 0.0054, 0.0126, 0.0061, 0.0096, 0.0031, 0.0106,\n",
      "        0.0044, 0.0026, 0.0042, 0.0090, 0.0089, 0.0070, 0.0045, 0.0076, 0.0087,\n",
      "        0.0031, 0.0097, 0.0175, 0.0055, 0.0047, 0.0076, 0.0113, 0.0212, 0.0031,\n",
      "        0.0106, 0.0150, 0.0073, 0.0038, 0.0054, 0.0175, 0.0050, 0.0076, 0.0150,\n",
      "        0.0183, 0.0082, 0.0051, 0.0048, 0.0027, 0.0125, 0.0053, 0.0066, 0.0066,\n",
      "        0.0034, 0.0049, 0.0081, 0.0048, 0.0052, 0.0196, 0.0074, 0.0057, 0.0118,\n",
      "        0.0064, 0.0045, 0.0087, 0.0079, 0.0060, 0.0124, 0.0084, 0.0035, 0.0202,\n",
      "        0.0048, 0.0039, 0.0057, 0.0038, 0.0089, 0.0051, 0.0051, 0.0078, 0.0069,\n",
      "        0.0099, 0.0042, 0.0088, 0.0039, 0.0035, 0.0047, 0.0062, 0.0043, 0.0051,\n",
      "        0.0174, 0.0041, 0.0070, 0.0116, 0.0093, 0.0169, 0.0097, 0.0049, 0.0094,\n",
      "        0.0161, 0.0106, 0.0086, 0.0144, 0.0207, 0.0066, 0.0138, 0.0069, 0.0063,\n",
      "        0.0037, 0.0089, 0.0088, 0.0029, 0.0055, 0.0079, 0.0065, 0.0072, 0.0218,\n",
      "        0.0030, 0.0105, 0.0038, 0.0126, 0.0098, 0.0155, 0.0042, 0.0043, 0.0173,\n",
      "        0.0106, 0.0117, 0.0048, 0.0118, 0.0064, 0.0051, 0.0031, 0.0053, 0.0139,\n",
      "        0.0128, 0.0098, 0.0078, 0.0086, 0.0104, 0.0096, 0.0046, 0.0081, 0.0059,\n",
      "        0.0080, 0.0152, 0.0124, 0.0064, 0.0218, 0.0041, 0.0026, 0.0044, 0.0037,\n",
      "        0.0108, 0.0059, 0.0108, 0.0050, 0.0067, 0.0092, 0.0123, 0.0047, 0.0086,\n",
      "        0.0109, 0.0047, 0.0054, 0.0064, 0.0096, 0.0043, 0.0028, 0.0041, 0.0047,\n",
      "        0.0047, 0.0074, 0.0037, 0.0053, 0.0031, 0.0036, 0.0022, 0.0068, 0.0031,\n",
      "        0.0034, 0.0109, 0.0049, 0.0063, 0.0095, 0.0053, 0.0058, 0.0134, 0.0164,\n",
      "        0.0072, 0.0106, 0.0035, 0.0088, 0.0073, 0.0058, 0.0053, 0.0070, 0.0063,\n",
      "        0.0225, 0.0052, 0.0062, 0.0112, 0.0032, 0.0025, 0.0057, 0.0047, 0.0113,\n",
      "        0.0044, 0.0069, 0.0043, 0.0084, 0.0023, 0.0041, 0.0052, 0.0037, 0.0075,\n",
      "        0.0062, 0.0041, 0.0148, 0.0069, 0.0043, 0.0065, 0.0295, 0.0117, 0.0132,\n",
      "        0.0040, 0.0101, 0.0212, 0.0044, 0.0040, 0.0052, 0.0081, 0.0115, 0.0067,\n",
      "        0.0083, 0.0053, 0.0093, 0.0112, 0.0064, 0.0030, 0.0082, 0.0052, 0.0029,\n",
      "        0.0073, 0.0093, 0.0038, 0.0075, 0.0042, 0.0066, 0.0096, 0.0125, 0.0170,\n",
      "        0.0039, 0.0110, 0.0077, 0.0082, 0.0038, 0.0037, 0.0117, 0.0055, 0.0029,\n",
      "        0.0113, 0.0147, 0.0022, 0.0024, 0.0030, 0.0035, 0.0076, 0.0036, 0.0080,\n",
      "        0.0114, 0.0058, 0.0115, 0.0092, 0.0035, 0.0080, 0.0071, 0.0049, 0.0043,\n",
      "        0.0097, 0.0049, 0.0046, 0.0030, 0.0028, 0.0117, 0.0054, 0.0206, 0.0148,\n",
      "        0.0079, 0.0031, 0.0109, 0.0168, 0.0144, 0.0048, 0.0101, 0.0151, 0.0140,\n",
      "        0.0127, 0.0043, 0.0088, 0.0235, 0.0173, 0.0059, 0.0221, 0.0126, 0.0099,\n",
      "        0.0080, 0.0189, 0.0073, 0.0097, 0.0073, 0.0077, 0.0049, 0.0065, 0.0071,\n",
      "        0.0047, 0.0046, 0.0072, 0.0077, 0.0037, 0.0045, 0.0119, 0.0090, 0.0093,\n",
      "        0.0073, 0.0129, 0.0048, 0.0060, 0.0033, 0.0063, 0.0128, 0.0131, 0.0029,\n",
      "        0.0106, 0.0162, 0.0046, 0.0032, 0.0069, 0.0110], dtype=torch.float64),\n",
      "       zero_point=tensor([0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0,\n",
      "        0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0,\n",
      "        0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0,\n",
      "        0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0,\n",
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      "       quantization_scheme=torch.per_channel_affine,\n",
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      "       axis=0)), ('features.12.block.2.fc1.bias', Parameter containing:\n",
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      "       axis=0)), ('features.12.block.3.0.bias', Parameter containing:\n",
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      "         0.2508, -0.0464, -0.1021,  0.0639, -0.1279,  0.0984,  0.1609,  0.0839])), ('features.12.block.3.0.scale', tensor(0.2204)), ('features.12.block.3.0.zero_point', tensor(65)), ('features.12.skip_add.scale', tensor(0.3830)), ('features.12.skip_add.zero_point', tensor(64)), ('features.13.block.0.0.weight', tensor([[[[ 0.0145]],\n",
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      "       quantization_scheme=torch.per_channel_affine,\n",
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      "       axis=0)), ('features.13.block.0.0.bias', Parameter containing:\n",
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      "        -5.9812e-02, -7.9586e-02])), ('features.13.block.0.0.scale', tensor(0.2147)), ('features.13.block.0.0.zero_point', tensor(62)), ('features.13.block.0.2.scale', tensor(0.1054)), ('features.13.block.0.2.zero_point', tensor(4)), ('features.13.block.1.0.weight', tensor([[[[ 0.0057,  0.1884,  0.0457,  0.0114,  0.1085],\n",
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      "\n",
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      "\n",
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      "          [-0.2125, -0.0875, -0.1000, -0.0708,  0.0000]]],\n",
      "\n",
      "\n",
      "        ...,\n",
      "\n",
      "\n",
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      "\n",
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      "\n",
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      "       size=(672, 1, 5, 5), dtype=torch.qint8,\n",
      "       quantization_scheme=torch.per_channel_affine,\n",
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      "        0.0032, 0.0213, 0.0056, 0.0049, 0.0029, 0.0047, 0.0076, 0.0042, 0.0013,\n",
      "        0.0026, 0.0043, 0.0030, 0.0178, 0.0020, 0.0030, 0.0090, 0.0031, 0.0037,\n",
      "        0.0054, 0.0021, 0.0032, 0.0107, 0.0019, 0.0035, 0.0039, 0.0059, 0.0029,\n",
      "        0.0017, 0.0033, 0.0042, 0.0010, 0.0048, 0.0027, 0.0038, 0.0052, 0.0208,\n",
      "        0.0021, 0.0038, 0.0027, 0.0049, 0.0028, 0.0037, 0.0074, 0.0060, 0.0033,\n",
      "        0.0107, 0.0038, 0.0052, 0.0024, 0.0044, 0.0033, 0.0054, 0.0024, 0.0049,\n",
      "        0.0034, 0.0028, 0.0070, 0.0064, 0.0061, 0.0038, 0.0020, 0.0021, 0.0040,\n",
      "        0.0061, 0.0016, 0.0027, 0.0031, 0.0050, 0.0038, 0.0097, 0.0021, 0.0064,\n",
      "        0.0022, 0.0086, 0.0040, 0.0138, 0.0050, 0.0037, 0.0042, 0.0079, 0.0031,\n",
      "        0.0032, 0.0027, 0.0027, 0.0020, 0.0092, 0.0105, 0.0045, 0.0068, 0.0020,\n",
      "        0.0025, 0.0064, 0.0034, 0.0122, 0.0013, 0.0037, 0.0029, 0.0090, 0.0016,\n",
      "        0.0070, 0.0025, 0.0021, 0.0023, 0.0025, 0.0052, 0.0019, 0.0023, 0.0045,\n",
      "        0.0015, 0.0055, 0.0078, 0.0042, 0.0020, 0.0021, 0.0033, 0.0034, 0.0100,\n",
      "        0.0055, 0.0028, 0.0025, 0.0106, 0.0017, 0.0208, 0.0050, 0.0016, 0.0025,\n",
      "        0.0022, 0.0045, 0.0035, 0.0023, 0.0027, 0.0031, 0.0032, 0.0043, 0.0061,\n",
      "        0.0042, 0.0097, 0.0019, 0.0016, 0.0053, 0.0051, 0.0016, 0.0061, 0.0052,\n",
      "        0.0064, 0.0015, 0.0031, 0.0044, 0.0012, 0.0067, 0.0096, 0.0034, 0.0031,\n",
      "        0.0026, 0.0052, 0.0046, 0.0021, 0.0041, 0.0050, 0.0091, 0.0039, 0.0026,\n",
      "        0.0060, 0.0016, 0.0085, 0.0070, 0.0040, 0.0040, 0.0060, 0.0079, 0.0034,\n",
      "        0.0058, 0.0051, 0.0125, 0.0043, 0.0031, 0.0029, 0.0027, 0.0046, 0.0038,\n",
      "        0.0067, 0.0069, 0.0044, 0.0052, 0.0046, 0.0039, 0.0017, 0.0016, 0.0142,\n",
      "        0.0015, 0.0038, 0.0067, 0.0032, 0.0030, 0.0053, 0.0017, 0.0018, 0.0077,\n",
      "        0.0029, 0.0056, 0.0086, 0.0038, 0.0031, 0.0022, 0.0019, 0.0045, 0.0131,\n",
      "        0.0027, 0.0052, 0.0064, 0.0027, 0.0025, 0.0020, 0.0015, 0.0137, 0.0027,\n",
      "        0.0036, 0.0095, 0.0021, 0.0025, 0.0047, 0.0054, 0.0043, 0.0061, 0.0140,\n",
      "        0.0039, 0.0057, 0.0043, 0.0032, 0.0048, 0.0025, 0.0237, 0.0028, 0.0030,\n",
      "        0.0023, 0.0015, 0.0046, 0.0061, 0.0030, 0.0037, 0.0059, 0.0017, 0.0074,\n",
      "        0.0031, 0.0037, 0.0027, 0.0042, 0.0076, 0.0026, 0.0020, 0.0100, 0.0056,\n",
      "        0.0079, 0.0021, 0.0032, 0.0056, 0.0103, 0.0023, 0.0054, 0.0024, 0.0033,\n",
      "        0.0116, 0.0056, 0.0119, 0.0018, 0.0022, 0.0027, 0.0019, 0.0017, 0.0020,\n",
      "        0.0045, 0.0067, 0.0032, 0.0043, 0.0076, 0.0057, 0.0033, 0.0031, 0.0022,\n",
      "        0.0031, 0.0037, 0.0032, 0.0200, 0.0034, 0.0075, 0.0070, 0.0036, 0.0028,\n",
      "        0.0040, 0.0036, 0.0025, 0.0089, 0.0025, 0.0074, 0.0036, 0.0071, 0.0034,\n",
      "        0.0034, 0.0032, 0.0049, 0.0021, 0.0043, 0.0012, 0.0049, 0.0031, 0.0061,\n",
      "        0.0121, 0.0028, 0.0039, 0.0145, 0.0050, 0.0032, 0.0028, 0.0057, 0.0026,\n",
      "        0.0015, 0.0039, 0.0110, 0.0065, 0.0028, 0.0038, 0.0058, 0.0045, 0.0075,\n",
      "        0.0036, 0.0067, 0.0052, 0.0057, 0.0045, 0.0014, 0.0065, 0.0083, 0.0013,\n",
      "        0.0045, 0.0054, 0.0047, 0.0120, 0.0063, 0.0058, 0.0038, 0.0070, 0.0046,\n",
      "        0.0078, 0.0022, 0.0016, 0.0017, 0.0033, 0.0049, 0.0013, 0.0026, 0.0153,\n",
      "        0.0028, 0.0045, 0.0105, 0.0017, 0.0027, 0.0030, 0.0054, 0.0025, 0.0027,\n",
      "        0.0021, 0.0021, 0.0016, 0.0016, 0.0069, 0.0040, 0.0023, 0.0044, 0.0020,\n",
      "        0.0061, 0.0019, 0.0191, 0.0037, 0.0067, 0.0067, 0.0031, 0.0032, 0.0013,\n",
      "        0.0033, 0.0038, 0.0063, 0.0034, 0.0029, 0.0062, 0.0035, 0.0061, 0.0080,\n",
      "        0.0051, 0.0046, 0.0059, 0.0054, 0.0041, 0.0080, 0.0050, 0.0020, 0.0049,\n",
      "        0.0054, 0.0031, 0.0020, 0.0072, 0.0108, 0.0049, 0.0107, 0.0041, 0.0067,\n",
      "        0.0034, 0.0016, 0.0021, 0.0036, 0.0030, 0.0101, 0.0022, 0.0029, 0.0059,\n",
      "        0.0048, 0.0016, 0.0070, 0.0077, 0.0029, 0.0063, 0.0063, 0.0078, 0.0031,\n",
      "        0.0020, 0.0100, 0.0084, 0.0030, 0.0024, 0.0050, 0.0058, 0.0043, 0.0041,\n",
      "        0.0025, 0.0056, 0.0031, 0.0041, 0.0063, 0.0061], dtype=torch.float64),\n",
      "       zero_point=tensor([0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0,\n",
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      "       axis=0)), ('features.13.block.1.0.bias', Parameter containing:\n",
      "tensor([-2.5676e-01,  3.5687e-01,  1.7172e-01,  1.1647e-01,  4.2993e-03,\n",
      "         1.2460e-01,  2.9960e-01,  6.2619e-02, -7.7060e-02, -1.9418e-01,\n",
      "        -1.1895e-01,  1.9471e-01, -1.4788e-01,  3.7855e-02, -1.6820e-02,\n",
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      "        -3.1584e-01, -3.1456e-01, -2.1818e-02, -7.8241e-02, -7.7758e-02,\n",
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      "        -8.5469e-02, -7.2104e-02,  2.3891e-01, -4.0127e-01, -2.9959e-01,\n",
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      "        -4.8540e-01, -4.1066e-01,  1.9134e-01,  3.4300e-02,  8.8662e-02,\n",
      "         2.9121e-01,  1.5770e-01,  4.3020e-01,  1.1099e-01, -2.8620e-01,\n",
      "        -1.6501e-02, -6.0856e-02, -3.2659e-01,  2.1539e-01, -1.0933e-01,\n",
      "         1.4367e-01,  8.4513e-02, -4.5818e-01, -9.4173e-02, -2.6545e-01,\n",
      "         3.7250e-01, -4.1544e-01, -2.4846e-02,  1.4327e-01,  2.7698e-01,\n",
      "         2.4624e-01, -6.2026e-02, -1.6579e-01, -1.9469e-01,  4.6252e-01,\n",
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      "        -2.3466e-01,  2.1865e-01,  1.8083e-01, -2.7716e-02, -8.7925e-02,\n",
      "         1.6181e-01, -3.8857e-02,  1.3288e-02, -5.5808e-02, -2.7204e-01,\n",
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      "        -8.3250e-02,  2.8849e-01, -8.5089e-02,  8.0717e-02,  3.5907e-03,\n",
      "        -2.4642e-01,  1.3735e-01,  2.3556e-01, -2.0501e-01,  3.6020e-01,\n",
      "         2.8568e-01, -2.5416e-01, -4.8258e-01, -1.0611e-01,  7.8532e-02,\n",
      "        -7.5854e-02,  2.4123e-01, -1.6350e-01,  1.8701e-01, -4.7888e-02,\n",
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      "       axis=0)), ('features.13.block.2.fc2.bias', Parameter containing:\n",
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      "       axis=0)), ('features.14.block.0.0.bias', Parameter containing:\n",
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      "\n",
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      "          [-0.1726,  0.0216,  0.1788,  0.1541,  0.1079],\n",
      "          [-0.2373,  0.0740,  0.1171,  0.0801,  0.0247],\n",
      "          [-0.2034, -0.0616,  0.1171,  0.0832, -0.0185],\n",
      "          [-0.3946, -0.2528, -0.2558, -0.2158, -0.1757]]],\n",
      "\n",
      "\n",
      "        ...,\n",
      "\n",
      "\n",
      "        [[[ 0.4907,  0.0359, -0.2334, -0.2693, -0.2214],\n",
      "          [ 0.3590,  0.2453, -0.2094, -0.2992, -0.3530],\n",
      "          [ 0.2752,  0.1316, -0.2034, -0.3052, -0.2274],\n",
      "          [ 0.7539,  0.0838, -0.1257, -0.2573, -0.1675],\n",
      "          [-0.1436,  0.0000, -0.0539, -0.1855, -0.1735]]],\n",
      "\n",
      "\n",
      "        [[[-0.0975,  0.0457,  0.3868,  0.1797, -0.0792],\n",
      "          [-0.1035,  0.0975,  0.0426,  0.2284, -0.0822],\n",
      "          [-0.2406, -0.2985, -0.0305, -0.1980, -0.1645],\n",
      "          [-0.1127, -0.2132, -0.2162, -0.2193, -0.3106],\n",
      "          [ 0.1523, -0.0487, -0.0640, -0.0822, -0.0335]]],\n",
      "\n",
      "\n",
      "        [[[-0.1337, -0.0545,  0.0495,  0.1089, -0.1535],\n",
      "          [-0.2278, -0.1485,  0.5892,  0.2624,  0.0594],\n",
      "          [-0.3763,  0.0693,  0.4258,  0.1882, -0.1040],\n",
      "          [-0.0842,  0.0297,  0.3268,  0.2575,  0.0693],\n",
      "          [-0.0941,  0.0594, -0.0891,  0.0198,  0.0248]]]],\n",
      "       size=(960, 1, 5, 5), dtype=torch.qint8,\n",
      "       quantization_scheme=torch.per_channel_affine,\n",
      "       scale=tensor([0.0033, 0.0028, 0.0031, 0.0061, 0.0043, 0.0042, 0.0042, 0.0046, 0.0058,\n",
      "        0.0026, 0.0032, 0.0040, 0.0033, 0.0020, 0.0059, 0.0044, 0.0123, 0.0070,\n",
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      "        0.0028, 0.0030, 0.0041, 0.0029, 0.0081, 0.0067, 0.0029, 0.0039, 0.0035,\n",
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      "        0.0036, 0.0078, 0.0127, 0.0024, 0.0065, 0.0048, 0.0025, 0.0060, 0.0046,\n",
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      "        0.0029, 0.0085, 0.0047, 0.0047, 0.0053, 0.0035, 0.0117, 0.0039, 0.0042,\n",
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      "        0.0065, 0.0055, 0.0030, 0.0044, 0.0047, 0.0061, 0.0056, 0.0116, 0.0044,\n",
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      "        0.0094, 0.0083, 0.0075, 0.0034, 0.0049, 0.0043, 0.0063, 0.0071, 0.0036,\n",
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      "        0.0076, 0.0065, 0.0035, 0.0041, 0.0021, 0.0047, 0.0068, 0.0063, 0.0037,\n",
      "        0.0024, 0.0047, 0.0172, 0.0036, 0.0051, 0.0018, 0.0046, 0.0015, 0.0043,\n",
      "        0.0046, 0.0043, 0.0031, 0.0039, 0.0047, 0.0109, 0.0053, 0.0087, 0.0050,\n",
      "        0.0085, 0.0062, 0.0047, 0.0020, 0.0039, 0.0015, 0.0022, 0.0039, 0.0059,\n",
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      "        0.0037, 0.0062, 0.0054, 0.0040, 0.0046, 0.0037, 0.0197, 0.0040, 0.0036,\n",
      "        0.0062, 0.0083, 0.0039, 0.0039, 0.0023, 0.0055, 0.0041, 0.0016, 0.0040,\n",
      "        0.0045, 0.0162, 0.0040, 0.0053, 0.0078, 0.0029, 0.0071, 0.0061, 0.0027,\n",
      "        0.0048, 0.0045, 0.0062, 0.0039, 0.0045, 0.0039, 0.0029, 0.0048, 0.0024,\n",
      "        0.0030, 0.0075, 0.0026, 0.0031, 0.0074, 0.0128, 0.0052, 0.0044, 0.0047,\n",
      "        0.0070, 0.0022, 0.0043, 0.0068, 0.0043, 0.0050, 0.0034, 0.0027, 0.0057,\n",
      "        0.0040, 0.0052, 0.0031, 0.0034, 0.0034, 0.0058, 0.0045, 0.0142, 0.0038,\n",
      "        0.0034, 0.0023, 0.0040, 0.0077, 0.0030, 0.0028, 0.0016, 0.0156, 0.0035,\n",
      "        0.0068, 0.0102, 0.0038, 0.0070, 0.0102, 0.0037, 0.0035, 0.0044, 0.0030,\n",
      "        0.0020, 0.0044, 0.0046, 0.0036, 0.0161, 0.0047, 0.0071, 0.0037, 0.0130,\n",
      "        0.0065, 0.0041, 0.0027, 0.0092, 0.0049, 0.0058, 0.0026, 0.0044, 0.0065,\n",
      "        0.0041, 0.0070, 0.0069, 0.0067, 0.0139, 0.0048, 0.0096, 0.0046, 0.0031,\n",
      "        0.0049, 0.0022, 0.0052, 0.0076, 0.0074, 0.0043, 0.0032, 0.0062, 0.0043,\n",
      "        0.0039, 0.0062, 0.0107, 0.0028, 0.0032, 0.0033, 0.0046, 0.0026, 0.0045,\n",
      "        0.0026, 0.0113, 0.0055, 0.0056, 0.0059, 0.0036, 0.0038, 0.0057, 0.0088,\n",
      "        0.0050, 0.0045, 0.0050, 0.0036, 0.0055, 0.0083, 0.0034, 0.0060, 0.0047,\n",
      "        0.0044, 0.0091, 0.0052, 0.0030, 0.0026, 0.0058, 0.0054, 0.0078, 0.0055,\n",
      "        0.0044, 0.0032, 0.0059, 0.0051, 0.0052, 0.0041, 0.0031, 0.0048, 0.0055,\n",
      "        0.0057, 0.0031, 0.0040, 0.0102, 0.0053, 0.0071, 0.0040, 0.0038, 0.0056,\n",
      "        0.0035, 0.0114, 0.0069, 0.0073, 0.0073, 0.0061, 0.0017, 0.0050, 0.0023,\n",
      "        0.0033, 0.0035, 0.0031, 0.0030, 0.0021, 0.0042, 0.0029, 0.0071, 0.0024,\n",
      "        0.0044, 0.0035, 0.0106, 0.0034, 0.0026, 0.0019, 0.0029, 0.0043, 0.0041,\n",
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      "        0.0035, 0.0046, 0.0030, 0.0040, 0.0035, 0.0110, 0.0050, 0.0017, 0.0041,\n",
      "        0.0046, 0.0032, 0.0042, 0.0060, 0.0028, 0.0053, 0.0038, 0.0046, 0.0068,\n",
      "        0.0054, 0.0170, 0.0045, 0.0033, 0.0073, 0.0071, 0.0050, 0.0057, 0.0028,\n",
      "        0.0082, 0.0068, 0.0069, 0.0025, 0.0064, 0.0051, 0.0030, 0.0056, 0.0084,\n",
      "        0.0037, 0.0033, 0.0052, 0.0078, 0.0030, 0.0051, 0.0061, 0.0037, 0.0046,\n",
      "        0.0027, 0.0082, 0.0130, 0.0063, 0.0031, 0.0031, 0.0053, 0.0027, 0.0057,\n",
      "        0.0042, 0.0092, 0.0039, 0.0043, 0.0060, 0.0057, 0.0041, 0.0079, 0.0026,\n",
      "        0.0088, 0.0040, 0.0052, 0.0039, 0.0104, 0.0045, 0.0069, 0.0045, 0.0085,\n",
      "        0.0065, 0.0036, 0.0087, 0.0055, 0.0108, 0.0035, 0.0067, 0.0031, 0.0035,\n",
      "        0.0047, 0.0033, 0.0069, 0.0042, 0.0085, 0.0070, 0.0066, 0.0023, 0.0053,\n",
      "        0.0029, 0.0034, 0.0070, 0.0043, 0.0027, 0.0033, 0.0031, 0.0039, 0.0049,\n",
      "        0.0020, 0.0070, 0.0040, 0.0035, 0.0032, 0.0034, 0.0045, 0.0046, 0.0036,\n",
      "        0.0061, 0.0025, 0.0039, 0.0029, 0.0032, 0.0041, 0.0029, 0.0049, 0.0127,\n",
      "        0.0039, 0.0059, 0.0074, 0.0026, 0.0024, 0.0048, 0.0044, 0.0056, 0.0047,\n",
      "        0.0043, 0.0030, 0.0060, 0.0026, 0.0118, 0.0045, 0.0072, 0.0086, 0.0037,\n",
      "        0.0050, 0.0033, 0.0042, 0.0025, 0.0032, 0.0029, 0.0022, 0.0038, 0.0163,\n",
      "        0.0031, 0.0081, 0.0052, 0.0053, 0.0036, 0.0039, 0.0069, 0.0055, 0.0045,\n",
      "        0.0032, 0.0049, 0.0042, 0.0026, 0.0058, 0.0039, 0.0061, 0.0029, 0.0023,\n",
      "        0.0016, 0.0040, 0.0044, 0.0025, 0.0046, 0.0037, 0.0043, 0.0046, 0.0038,\n",
      "        0.0072, 0.0067, 0.0043, 0.0053, 0.0055, 0.0021, 0.0037, 0.0023, 0.0062,\n",
      "        0.0029, 0.0043, 0.0039, 0.0050, 0.0053, 0.0089, 0.0042, 0.0039, 0.0056,\n",
      "        0.0046, 0.0039, 0.0046, 0.0053, 0.0096, 0.0042, 0.0077, 0.0145, 0.0112,\n",
      "        0.0025, 0.0120, 0.0032, 0.0041, 0.0059, 0.0031, 0.0038, 0.0038, 0.0026,\n",
      "        0.0042, 0.0076, 0.0033, 0.0045, 0.0071, 0.0044, 0.0057, 0.0038, 0.0045,\n",
      "        0.0023, 0.0032, 0.0032, 0.0028, 0.0043, 0.0039, 0.0027, 0.0034, 0.0085,\n",
      "        0.0043, 0.0137, 0.0027, 0.0037, 0.0058, 0.0047, 0.0176, 0.0043, 0.0108,\n",
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      "        0.0043, 0.0037, 0.0036, 0.0060, 0.0030, 0.0050], dtype=torch.float64),\n",
      "       zero_point=tensor([0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0,\n",
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      "       axis=0)), ('features.14.block.1.0.bias', Parameter containing:\n",
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      "       axis=0)), ('features.14.block.2.fc2.bias', Parameter containing:\n",
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      "       requires_grad=True)), ('features.14.block.2.fc2.scale', tensor(0.8665)), ('features.14.block.2.fc2.zero_point', tensor(63)), ('features.14.block.2.skip_mul.scale', tensor(0.1055)), ('features.14.block.2.skip_mul.zero_point', tensor(3)), ('features.14.block.3.0.weight', tensor([[[[-0.0121]],\n",
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      "       dtype=torch.float64),\n",
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      "       axis=0)), ('features.14.block.3.0.bias', Parameter containing:\n",
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      "         0.0162, -0.0200, -0.2130, -0.3434, -0.2388,  0.1799, -0.1672,  0.1836])), ('features.14.block.3.0.scale', tensor(0.2262)), ('features.14.block.3.0.zero_point', tensor(64)), ('features.14.skip_add.scale', tensor(0.3449)), ('features.14.skip_add.zero_point', tensor(68)), ('features.15.block.0.0.weight', tensor([[[[ 0.0026]],\n",
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      "         [[-0.0063]]]], size=(960, 160, 1, 1), dtype=torch.qint8,\n",
      "       quantization_scheme=torch.per_channel_affine,\n",
      "       scale=tensor([0.0003, 0.0004, 0.0005, 0.0005, 0.0003, 0.0005, 0.0003, 0.0004, 0.0003,\n",
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      "       axis=0)), ('features.15.block.0.0.bias', Parameter containing:\n",
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      "        -1.1413e-01, -1.9947e-02, -7.1967e-02, -1.0420e-01,  2.1063e-03,\n",
      "         5.6593e-02,  4.1043e-02, -1.9429e-01,  2.5317e-02, -1.5184e-01,\n",
      "         2.0451e-02,  3.5311e-02,  4.8163e-02,  1.7637e-01, -7.6852e-02,\n",
      "        -9.0991e-02,  5.8644e-03, -4.8925e-02,  5.4610e-02, -3.0988e-02,\n",
      "        -6.9154e-02, -1.5792e-01,  1.3175e-01,  1.9667e-03,  1.1177e-01,\n",
      "        -1.4742e-01,  3.0604e-02, -8.2019e-02,  1.2103e-01, -4.3329e-02,\n",
      "         4.5312e-02, -1.1393e-01, -1.7236e-01,  3.9285e-02, -6.1152e-02,\n",
      "        -6.5532e-02, -1.4668e-01,  1.9400e-02,  6.7185e-02, -1.2404e-01,\n",
      "         1.2539e-01, -1.1631e-01,  9.9734e-02,  9.8093e-02,  2.2778e-02,\n",
      "        -3.2166e-02, -1.1971e-01,  1.6256e-02,  6.4306e-02,  2.6826e-02,\n",
      "        -9.0411e-02,  9.4108e-02, -1.2232e-01, -1.3692e-01,  7.4164e-03,\n",
      "         1.8174e-02, -8.7488e-02,  8.1645e-02, -6.6154e-02, -1.0574e-01,\n",
      "        -5.3525e-02, -1.6103e-01, -3.0678e-01, -8.4031e-02,  1.4991e-01,\n",
      "        -1.6407e-01, -7.3551e-02, -1.4861e-01, -1.3629e-02, -5.7003e-02,\n",
      "        -1.0114e-02, -1.7439e-01, -6.7841e-02,  4.0244e-02, -1.2996e-01,\n",
      "         1.0283e-02,  7.7957e-02,  8.2040e-02,  8.1554e-02,  8.6358e-02,\n",
      "        -1.3754e-02, -5.4012e-02, -3.4198e-02,  5.8101e-02, -2.1033e-01,\n",
      "         3.4400e-02,  4.1336e-02, -1.6216e-01, -3.9234e-02,  7.4507e-02,\n",
      "         5.5202e-02,  1.4882e-01, -3.2514e-02,  7.0466e-02, -1.0707e-01,\n",
      "        -1.6059e-02, -1.6335e-01, -1.2909e-02,  2.1937e-02,  3.7346e-02,\n",
      "        -6.7864e-02,  9.0900e-02,  6.0126e-02, -1.8157e-02, -1.3365e-01])), ('features.15.block.0.0.scale', tensor(0.2215)), ('features.15.block.0.0.zero_point', tensor(61)), ('features.15.block.0.2.scale', tensor(0.1133)), ('features.15.block.0.2.zero_point', tensor(3)), ('features.15.block.1.0.weight', tensor([[[[ 0.1025,  0.2221,  0.1880,  0.2051,  0.0171],\n",
      "          [ 0.1367,  1.0936,  0.2392,  0.6323, -0.7006],\n",
      "          [-0.4443,  0.3418,  0.5297,  0.1367, -0.4443],\n",
      "          [-0.4614,  1.4354,  0.2905, -1.0595, -0.4272],\n",
      "          [ 0.0342,  1.7601,  1.5379, -0.7348, -0.7690]]],\n",
      "\n",
      "\n",
      "        [[[-0.1710, -0.1624, -0.1881, -0.1966, -0.5985],\n",
      "          [-0.1966, -0.2565, -0.3762, -0.2052, -0.2052],\n",
      "          [-0.6327, -0.3933, -0.5215, -0.4018, -0.2223],\n",
      "          [-0.4018, -0.4873, -0.3676, -0.2907,  1.0773],\n",
      "          [-0.4702, -0.4531, -0.4360, -0.1197,  0.5386]]],\n",
      "\n",
      "\n",
      "        [[[-0.0426,  0.2612, -0.1013,  0.3305, -0.0267],\n",
      "          [-0.6611, -0.3572, -0.1493, -0.0320, -0.0746],\n",
      "          [-0.3732, -0.0853,  0.1973,  0.2399,  0.1546],\n",
      "          [-0.0107, -0.0853,  0.0053, -0.0053, -0.0053],\n",
      "          [ 0.0107,  0.2559,  0.0267, -0.6504, -0.3252]]],\n",
      "\n",
      "\n",
      "        ...,\n",
      "\n",
      "\n",
      "        [[[ 0.1273,  0.1208,  0.1665,  0.1077,  0.1958],\n",
      "          [ 0.2187,  0.4145,  0.2089,  0.3101,  0.3917],\n",
      "          [ 0.0326,  0.0424,  0.1077,  0.0000,  0.1012],\n",
      "          [-0.0098, -0.0098, -0.0881, -0.2938, -0.0065],\n",
      "          [-0.0979, -0.2481, -0.0228, -0.1763, -0.1240]]],\n",
      "\n",
      "\n",
      "        [[[ 0.0279,  0.4431, -0.0872, -0.1465, -0.3280],\n",
      "          [-0.0035, -0.0837, -0.1082, -0.0733, -0.0907],\n",
      "          [-0.0105,  0.0558, -0.0907, -0.0977, -0.3803],\n",
      "          [ 0.1256,  0.0314, -0.1326, -0.0698, -0.3489],\n",
      "          [ 0.0768,  0.1221, -0.1396, -0.1675, -0.0907]]],\n",
      "\n",
      "\n",
      "        [[[-0.0774, -0.4043, -0.1899, -0.0984,  0.1617],\n",
      "          [-0.0316, -0.2110, -0.0352,  0.0352,  0.2531],\n",
      "          [ 0.1723, -0.3868, -0.1899,  0.1090,  0.4465],\n",
      "          [-0.1723, -0.1723, -0.1231,  0.0176,  0.2145],\n",
      "          [-0.2180, -0.2180, -0.2074,  0.0105,  0.2180]]]],\n",
      "       size=(960, 1, 5, 5), dtype=torch.qint8,\n",
      "       quantization_scheme=torch.per_channel_affine,\n",
      "       scale=tensor([0.0171, 0.0085, 0.0053, 0.0042, 0.0146, 0.0030, 0.0070, 0.0048, 0.0097,\n",
      "        0.0022, 0.0080, 0.0127, 0.0060, 0.0168, 0.0028, 0.0045, 0.0097, 0.0032,\n",
      "        0.0057, 0.0026, 0.0027, 0.0030, 0.0031, 0.0048, 0.0024, 0.0039, 0.0026,\n",
      "        0.0028, 0.0028, 0.0088, 0.0027, 0.0049, 0.0110, 0.0025, 0.0071, 0.0055,\n",
      "        0.0121, 0.0074, 0.0036, 0.0169, 0.0220, 0.0039, 0.0033, 0.0023, 0.0073,\n",
      "        0.0031, 0.0025, 0.0141, 0.0023, 0.0029, 0.0033, 0.0049, 0.0029, 0.0027,\n",
      "        0.0036, 0.0034, 0.0023, 0.0043, 0.0014, 0.0036, 0.0103, 0.0031, 0.0012,\n",
      "        0.0045, 0.0110, 0.0030, 0.0031, 0.0042, 0.0091, 0.0099, 0.0026, 0.0032,\n",
      "        0.0028, 0.0056, 0.0043, 0.0026, 0.0135, 0.0037, 0.0129, 0.0129, 0.0087,\n",
      "        0.0039, 0.0154, 0.0034, 0.0029, 0.0034, 0.0099, 0.0026, 0.0052, 0.0051,\n",
      "        0.0026, 0.0079, 0.0033, 0.0048, 0.0079, 0.0121, 0.0217, 0.0109, 0.0066,\n",
      "        0.0023, 0.0013, 0.0066, 0.0027, 0.0052, 0.0057, 0.0020, 0.0060, 0.0021,\n",
      "        0.0068, 0.0134, 0.0025, 0.0051, 0.0068, 0.0024, 0.0063, 0.0101, 0.0045,\n",
      "        0.0034, 0.0021, 0.0026, 0.0050, 0.0081, 0.0035, 0.0035, 0.0036, 0.0050,\n",
      "        0.0036, 0.0057, 0.0033, 0.0025, 0.0039, 0.0030, 0.0020, 0.0035, 0.0045,\n",
      "        0.0019, 0.0063, 0.0072, 0.0028, 0.0062, 0.0044, 0.0019, 0.0040, 0.0026,\n",
      "        0.0063, 0.0035, 0.0062, 0.0101, 0.0070, 0.0026, 0.0122, 0.0025, 0.0099,\n",
      "        0.0018, 0.0017, 0.0246, 0.0020, 0.0158, 0.0034, 0.0134, 0.0025, 0.0057,\n",
      "        0.0068, 0.0080, 0.0101, 0.0017, 0.0020, 0.0054, 0.0023, 0.0022, 0.0027,\n",
      "        0.0029, 0.0083, 0.0074, 0.0150, 0.0057, 0.0067, 0.0030, 0.0035, 0.0020,\n",
      "        0.0030, 0.0029, 0.0031, 0.0058, 0.0042, 0.0039, 0.0033, 0.0021, 0.0039,\n",
      "        0.0063, 0.0112, 0.0071, 0.0185, 0.0032, 0.0027, 0.0021, 0.0126, 0.0079,\n",
      "        0.0198, 0.0092, 0.0043, 0.0054, 0.0036, 0.0108, 0.0021, 0.0041, 0.0036,\n",
      "        0.0076, 0.0117, 0.0064, 0.0041, 0.0037, 0.0068, 0.0168, 0.0031, 0.0024,\n",
      "        0.0038, 0.0039, 0.0053, 0.0044, 0.0113, 0.0031, 0.0033, 0.0081, 0.0035,\n",
      "        0.0020, 0.0036, 0.0022, 0.0036, 0.0016, 0.0082, 0.0146, 0.0024, 0.0066,\n",
      "        0.0232, 0.0058, 0.0031, 0.0022, 0.0088, 0.0021, 0.0049, 0.0026, 0.0046,\n",
      "        0.0038, 0.0037, 0.0106, 0.0028, 0.0077, 0.0025, 0.0020, 0.0044, 0.0023,\n",
      "        0.0183, 0.0038, 0.0027, 0.0102, 0.0131, 0.0029, 0.0025, 0.0076, 0.0142,\n",
      "        0.0125, 0.0030, 0.0118, 0.0028, 0.0038, 0.0080, 0.0033, 0.0028, 0.0082,\n",
      "        0.0017, 0.0036, 0.0027, 0.0026, 0.0017, 0.0058, 0.0162, 0.0028, 0.0025,\n",
      "        0.0027, 0.0068, 0.0076, 0.0034, 0.0113, 0.0033, 0.0102, 0.0082, 0.0072,\n",
      "        0.0060, 0.0064, 0.0093, 0.0028, 0.0028, 0.0024, 0.0107, 0.0091, 0.0070,\n",
      "        0.0031, 0.0048, 0.0030, 0.0149, 0.0028, 0.0022, 0.0028, 0.0060, 0.0108,\n",
      "        0.0134, 0.0046, 0.0145, 0.0101, 0.0022, 0.0054, 0.0035, 0.0155, 0.0018,\n",
      "        0.0149, 0.0033, 0.0069, 0.0028, 0.0086, 0.0147, 0.0030, 0.0051, 0.0029,\n",
      "        0.0061, 0.0117, 0.0049, 0.0112, 0.0150, 0.0072, 0.0060, 0.0056, 0.0029,\n",
      "        0.0020, 0.0045, 0.0165, 0.0106, 0.0045, 0.0065, 0.0038, 0.0021, 0.0097,\n",
      "        0.0055, 0.0023, 0.0054, 0.0017, 0.0026, 0.0027, 0.0053, 0.0201, 0.0020,\n",
      "        0.0048, 0.0040, 0.0098, 0.0028, 0.0119, 0.0029, 0.0017, 0.0019, 0.0040,\n",
      "        0.0029, 0.0113, 0.0094, 0.0036, 0.0032, 0.0027, 0.0108, 0.0075, 0.0023,\n",
      "        0.0028, 0.0038, 0.0028, 0.0039, 0.0035, 0.0030, 0.0119, 0.0046, 0.0028,\n",
      "        0.0061, 0.0023, 0.0043, 0.0177, 0.0026, 0.0057, 0.0053, 0.0057, 0.0052,\n",
      "        0.0066, 0.0019, 0.0051, 0.0022, 0.0031, 0.0110, 0.0064, 0.0035, 0.0052,\n",
      "        0.0027, 0.0096, 0.0021, 0.0066, 0.0033, 0.0062, 0.0036, 0.0029, 0.0042,\n",
      "        0.0085, 0.0049, 0.0028, 0.0029, 0.0036, 0.0020, 0.0112, 0.0033, 0.0078,\n",
      "        0.0067, 0.0041, 0.0020, 0.0111, 0.0023, 0.0051, 0.0044, 0.0130, 0.0031,\n",
      "        0.0048, 0.0045, 0.0015, 0.0031, 0.0089, 0.0034, 0.0021, 0.0030, 0.0049,\n",
      "        0.0031, 0.0040, 0.0040, 0.0034, 0.0051, 0.0100, 0.0018, 0.0112, 0.0041,\n",
      "        0.0032, 0.0024, 0.0035, 0.0053, 0.0102, 0.0104, 0.0134, 0.0023, 0.0053,\n",
      "        0.0109, 0.0023, 0.0156, 0.0100, 0.0075, 0.0087, 0.0022, 0.0030, 0.0203,\n",
      "        0.0027, 0.0108, 0.0027, 0.0054, 0.0101, 0.0107, 0.0030, 0.0029, 0.0175,\n",
      "        0.0053, 0.0049, 0.0108, 0.0018, 0.0037, 0.0086, 0.0037, 0.0036, 0.0066,\n",
      "        0.0064, 0.0038, 0.0190, 0.0061, 0.0119, 0.0072, 0.0161, 0.0013, 0.0034,\n",
      "        0.0170, 0.0016, 0.0100, 0.0028, 0.0030, 0.0071, 0.0078, 0.0034, 0.0072,\n",
      "        0.0045, 0.0015, 0.0030, 0.0032, 0.0015, 0.0071, 0.0025, 0.0082, 0.0119,\n",
      "        0.0055, 0.0133, 0.0059, 0.0024, 0.0155, 0.0029, 0.0039, 0.0055, 0.0024,\n",
      "        0.0035, 0.0119, 0.0045, 0.0074, 0.0019, 0.0033, 0.0040, 0.0158, 0.0078,\n",
      "        0.0014, 0.0036, 0.0090, 0.0049, 0.0028, 0.0067, 0.0032, 0.0036, 0.0167,\n",
      "        0.0022, 0.0111, 0.0066, 0.0082, 0.0021, 0.0020, 0.0052, 0.0039, 0.0044,\n",
      "        0.0077, 0.0114, 0.0039, 0.0071, 0.0092, 0.0028, 0.0075, 0.0021, 0.0124,\n",
      "        0.0033, 0.0121, 0.0033, 0.0044, 0.0030, 0.0091, 0.0041, 0.0060, 0.0044,\n",
      "        0.0039, 0.0056, 0.0045, 0.0055, 0.0076, 0.0029, 0.0104, 0.0030, 0.0042,\n",
      "        0.0064, 0.0026, 0.0043, 0.0046, 0.0166, 0.0050, 0.0043, 0.0105, 0.0160,\n",
      "        0.0043, 0.0145, 0.0023, 0.0035, 0.0082, 0.0032, 0.0030, 0.0066, 0.0041,\n",
      "        0.0049, 0.0136, 0.0036, 0.0035, 0.0049, 0.0029, 0.0066, 0.0047, 0.0029,\n",
      "        0.0073, 0.0017, 0.0048, 0.0035, 0.0050, 0.0044, 0.0027, 0.0059, 0.0059,\n",
      "        0.0031, 0.0053, 0.0021, 0.0027, 0.0024, 0.0058, 0.0034, 0.0034, 0.0133,\n",
      "        0.0079, 0.0078, 0.0118, 0.0163, 0.0021, 0.0038, 0.0035, 0.0074, 0.0023,\n",
      "        0.0059, 0.0062, 0.0033, 0.0044, 0.0026, 0.0018, 0.0024, 0.0039, 0.0031,\n",
      "        0.0046, 0.0067, 0.0254, 0.0025, 0.0021, 0.0110, 0.0081, 0.0045, 0.0073,\n",
      "        0.0065, 0.0096, 0.0023, 0.0096, 0.0025, 0.0098, 0.0022, 0.0027, 0.0068,\n",
      "        0.0147, 0.0217, 0.0029, 0.0026, 0.0035, 0.0048, 0.0068, 0.0080, 0.0049,\n",
      "        0.0027, 0.0019, 0.0035, 0.0067, 0.0082, 0.0053, 0.0051, 0.0021, 0.0026,\n",
      "        0.0029, 0.0049, 0.0083, 0.0074, 0.0025, 0.0025, 0.0039, 0.0113, 0.0021,\n",
      "        0.0025, 0.0052, 0.0035, 0.0028, 0.0024, 0.0049, 0.0027, 0.0040, 0.0068,\n",
      "        0.0024, 0.0148, 0.0047, 0.0024, 0.0104, 0.0030, 0.0017, 0.0079, 0.0078,\n",
      "        0.0043, 0.0036, 0.0064, 0.0026, 0.0038, 0.0234, 0.0054, 0.0029, 0.0033,\n",
      "        0.0061, 0.0144, 0.0032, 0.0095, 0.0034, 0.0069, 0.0025, 0.0032, 0.0043,\n",
      "        0.0116, 0.0021, 0.0035, 0.0072, 0.0223, 0.0063, 0.0058, 0.0101, 0.0046,\n",
      "        0.0097, 0.0031, 0.0072, 0.0112, 0.0038, 0.0023, 0.0034, 0.0041, 0.0049,\n",
      "        0.0046, 0.0039, 0.0056, 0.0142, 0.0033, 0.0070, 0.0119, 0.0027, 0.0044,\n",
      "        0.0142, 0.0065, 0.0121, 0.0042, 0.0044, 0.0125, 0.0075, 0.0027, 0.0024,\n",
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      "        0.0082, 0.0038, 0.0050, 0.0026, 0.0037, 0.0022, 0.0028, 0.0173, 0.0024,\n",
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      "        0.0033, 0.0024, 0.0046, 0.0040, 0.0035, 0.0046, 0.0125, 0.0052, 0.0190,\n",
      "        0.0062, 0.0022, 0.0032, 0.0032, 0.0039, 0.0040, 0.0076, 0.0024, 0.0027,\n",
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      "        0.0115, 0.0038, 0.0121, 0.0047, 0.0061, 0.0053, 0.0074, 0.0034, 0.0108,\n",
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      "        0.0051, 0.0080, 0.0017, 0.0033, 0.0035, 0.0035], dtype=torch.float64),\n",
      "       zero_point=tensor([0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0,\n",
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      "       axis=0)), ('features.15.block.1.0.bias', Parameter containing:\n",
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      "         [[-0.0542]],\n",
      "\n",
      "         [[-0.0510]]],\n",
      "\n",
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      "        [[[-0.0311]],\n",
      "\n",
      "         [[-0.0402]],\n",
      "\n",
      "         [[ 0.0168]],\n",
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      "         ...,\n",
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      "         [[-0.0013]],\n",
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      "         [[-0.0855]],\n",
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      "         [[ 0.0117]]],\n",
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      "        ...,\n",
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      "        [[[-0.0532]],\n",
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      "         [[-0.0294]],\n",
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      "         [[ 0.0042]],\n",
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      "         ...,\n",
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      "         [[-0.0671]],\n",
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      "        [[[-0.0398]],\n",
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      "         [[ 0.0269]],\n",
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      "        [[[ 0.0609]],\n",
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      "         [[-0.0047]],\n",
      "\n",
      "         [[ 0.0843]]]], size=(960, 240, 1, 1), dtype=torch.qint8,\n",
      "       quantization_scheme=torch.per_channel_affine,\n",
      "       scale=tensor([0.0015, 0.0016, 0.0013, 0.0015, 0.0015, 0.0011, 0.0017, 0.0013, 0.0015,\n",
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      "       axis=0)), ('features.15.block.2.fc2.bias', Parameter containing:\n",
      "tensor([-5.9907e-02, -1.2160e-01, -5.5622e-02,  5.0877e-02, -4.6977e-02,\n",
      "        -5.4514e-02,  1.5846e-02, -7.6371e-02, -6.8444e-02, -3.2462e-02,\n",
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      "       dtype=torch.float64),\n",
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      "       axis=0)), ('features.16.0.bias', Parameter containing:\n",
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      "        -1.8228e-01, -1.5478e-01, -6.5705e-02, -5.1625e-02, -1.4706e-01,\n",
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      "         2.1013e-02, -9.2702e-02, -1.5964e-01, -2.0140e-01, -4.9485e-02,\n",
      "        -2.5912e-01, -1.4241e-01,  4.5260e-03, -7.7201e-02, -1.3311e-01,\n",
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      "        -5.0259e-02,  1.7025e-01,  4.1750e-02,  3.0711e-02, -9.8964e-03,\n",
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      "        -1.0944e-01, -1.5155e-01, -1.2036e-01, -1.5613e-01, -1.5494e-01,\n",
      "        -1.1910e-01,  4.1725e-02, -7.3056e-02, -1.6230e-01,  4.8277e-02,\n",
      "        -7.0866e-02, -2.2456e-01, -3.4026e-02, -3.1094e-02, -9.4508e-02,\n",
      "        -1.4185e-01,  6.6538e-02, -1.7685e-01, -1.0207e-01, -1.6033e-01,\n",
      "         2.9742e-02, -1.1068e-01, -4.7421e-02, -5.4555e-03, -2.4650e-01,\n",
      "        -6.2615e-02, -1.8960e-01, -1.1933e-01,  1.0071e-02, -1.0506e-01,\n",
      "        -1.7549e-01, -1.8829e-01, -7.7103e-02, -8.2797e-02, -1.3408e-01,\n",
      "        -2.1842e-01,  2.1110e-01, -1.2585e-01, -1.4092e-01, -6.8298e-02,\n",
      "        -1.1520e-01, -1.8404e-01, -3.0757e-02, -8.7709e-02,  1.4203e-01,\n",
      "        -1.7822e-01, -2.2514e-01, -1.2910e-01,  2.3753e-01, -2.2162e-01,\n",
      "         6.0784e-02, -3.5510e-02, -9.4397e-02, -9.6120e-02, -4.7683e-02,\n",
      "        -5.1068e-02,  1.6896e-01, -1.0024e-01, -2.0346e-01, -8.5879e-02,\n",
      "        -1.3367e-01, -8.7083e-02, -1.6614e-01, -1.7597e-01, -1.0936e-02,\n",
      "        -3.4724e-02, -1.3322e-01,  1.2652e-02, -8.9153e-02, -2.4577e-01])), ('features.16.0.scale', tensor(0.1837)), ('features.16.0.zero_point', tensor(68)), ('features.16.2.scale', tensor(0.0853)), ('features.16.2.zero_point', tensor(4)), ('classifier.0.scale', tensor(0.1833)), ('classifier.0.zero_point', tensor(71)), ('classifier.0._packed_params.dtype', torch.qint8), ('classifier.0._packed_params._packed_params', (tensor([[-0.1672, -0.0131,  0.0901,  ..., -0.0222,  0.0000, -0.0666],\n",
      "        [ 0.0247,  0.0153, -0.0377,  ..., -0.0047, -0.0318,  0.0047],\n",
      "        [-0.0534, -0.0320, -0.0614,  ...,  0.0173, -0.0480,  0.0133],\n",
      "        ...,\n",
      "        [ 0.0280, -0.0408,  0.0051,  ..., -0.0089,  0.0102, -0.0127],\n",
      "        [-0.0795, -0.0182, -0.0560,  ..., -0.0208, -0.0690, -0.0104],\n",
      "        [-0.0118,  0.0118, -0.0141,  ..., -0.0083,  0.0094,  0.0094]],\n",
      "       size=(1280, 960), dtype=torch.qint8,\n",
      "       quantization_scheme=torch.per_channel_affine,\n",
      "       scale=tensor([0.0013, 0.0012, 0.0013,  ..., 0.0013, 0.0013, 0.0012],\n",
      "       dtype=torch.float64),\n",
      "       zero_point=tensor([0, 0, 0,  ..., 0, 0, 0]), axis=0), Parameter containing:\n",
      "tensor([-0.0843, -0.0702, -0.0635,  ...,  0.0347, -0.0657, -0.0396],\n",
      "       requires_grad=True))), ('classifier.1.scale', tensor(0.0771)), ('classifier.1.zero_point', tensor(5)), ('classifier.3.scale', tensor(0.5151)), ('classifier.3.zero_point', tensor(72)), ('classifier.3._packed_params.dtype', torch.qint8), ('classifier.3._packed_params._packed_params', (tensor([[ 0.0160, -0.0016,  0.0623,  ..., -0.0399, -0.0224,  0.0064],\n",
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      "        [ 0.0174, -0.0236, -0.0473,  ..., -0.0995,  0.0261, -0.0734],\n",
      "        ...,\n",
      "        [ 0.0400,  0.0429, -0.0770,  ...,  0.0015,  0.0178, -0.0281],\n",
      "        [ 0.0077, -0.0031,  0.0707,  ..., -0.0154, -0.1291, -0.0046],\n",
      "        [ 0.0377, -0.0172, -0.0395,  ..., -0.0223,  0.0618, -0.0275]],\n",
      "       size=(90, 1280), dtype=torch.qint8,\n",
      "       quantization_scheme=torch.per_channel_affine,\n",
      "       scale=tensor([0.0016, 0.0018, 0.0012, 0.0012, 0.0015, 0.0017, 0.0016, 0.0014, 0.0016,\n",
      "        0.0018, 0.0016, 0.0012, 0.0015, 0.0017, 0.0016, 0.0012, 0.0018, 0.0017,\n",
      "        0.0015, 0.0015, 0.0018, 0.0018, 0.0017, 0.0015, 0.0016, 0.0017, 0.0016,\n",
      "        0.0014, 0.0016, 0.0014, 0.0015, 0.0016, 0.0018, 0.0016, 0.0017, 0.0015,\n",
      "        0.0016, 0.0017, 0.0018, 0.0014, 0.0015, 0.0015, 0.0016, 0.0017, 0.0015,\n",
      "        0.0018, 0.0015, 0.0015, 0.0016, 0.0015, 0.0018, 0.0016, 0.0017, 0.0015,\n",
      "        0.0017, 0.0018, 0.0018, 0.0013, 0.0016, 0.0012, 0.0015, 0.0015, 0.0014,\n",
      "        0.0014, 0.0014, 0.0016, 0.0017, 0.0015, 0.0015, 0.0015, 0.0017, 0.0016,\n",
      "        0.0017, 0.0016, 0.0015, 0.0015, 0.0017, 0.0014, 0.0014, 0.0015, 0.0015,\n",
      "        0.0016, 0.0016, 0.0015, 0.0015, 0.0016, 0.0015, 0.0015, 0.0015, 0.0017],\n",
      "       dtype=torch.float64),\n",
      "       zero_point=tensor([0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0,\n",
      "        0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0,\n",
      "        0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0,\n",
      "        0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0]),\n",
      "       axis=0), Parameter containing:\n",
      "tensor([ 0.0422,  0.0363, -0.0290, -0.0330,  0.0066,  0.0245,  0.0160,  0.0120,\n",
      "        -0.0146, -0.0579,  0.0248, -0.0280,  0.0021,  0.0096, -0.0040, -0.0288,\n",
      "        -0.0278,  0.0401, -0.0321, -0.0659,  0.0967, -0.0120, -0.0177,  0.0120,\n",
      "         0.0392,  0.0250,  0.0116,  0.0225,  0.0498, -0.0360, -0.0325, -0.0622,\n",
      "         0.0239, -0.0815, -0.0376,  0.0432, -0.1010, -0.0224,  0.0934,  0.0306,\n",
      "        -0.0364, -0.0203,  0.0457,  0.0219,  0.0231, -0.0225, -0.0235, -0.0197,\n",
      "        -0.0874, -0.0354,  0.0223, -0.0062, -0.0433, -0.0242,  0.0598,  0.0492,\n",
      "         0.0397,  0.0570, -0.0273, -0.0192,  0.0097,  0.0181, -0.0511, -0.0260,\n",
      "        -0.0693, -0.0131, -0.0130, -0.0610, -0.0281, -0.0085,  0.0561, -0.0173,\n",
      "        -0.0061, -0.0016, -0.0837,  0.0013,  0.0690, -0.0638,  0.0077,  0.0560,\n",
      "         0.0643, -0.0183,  0.1058, -0.0594,  0.1373, -0.0542, -0.0263,  0.0822,\n",
      "        -0.0165,  0.0134], requires_grad=True))), ('quant.scale', tensor([0.0079])), ('quant.zero_point', tensor([0]))])\n"
     ]
    }
   ],
   "source": [
    "quantized_model = torch.quantization.convert(model.cpu().eval(), inplace=False)\n",
    "quantized_model.eval()\n",
    "print(quantized_model.state_dict())\n",
    "s = time.strftime('%Y_%m_%d_%H_%M_%S', time.localtime())\n",
    "torch.save(quantized_model.state_dict(), f'save_model/int8/ecgid_model_{s}.pt')"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 7,
   "id": "32cf35c0",
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "None\n"
     ]
    }
   ],
   "source": [
    "model = mobilenet_v3_large()\n",
    "state_dict = torch.load('/home/yangaowei/ecg_id/save_model/int8/ecgid_model_2024_05_18_15_11_23.pt')\n",
    "model.qconfig = get_default_qat_qconfig(\"fbgemm\")\n",
    "# prepare_qat(model, inplace=True)\n",
    "print(state_dict)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 14,
   "id": "753f0644-def1-4c54-b8df-8fd6234d3ead",
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "None\n"
     ]
    }
   ],
   "source": [
    "print(best_model_wts)\n",
    "s = time.strftime('%Y_%m_%d_%H_%M_%S', time.localtime())\n",
    "torch.save(best_model_wts, f'save_model/int8/ecgid_model_{s}.pt')"
   ]
  }
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